diff --git a/backend/app/analysis/common.py b/backend/app/analysis/common.py
index 3c278c5b..b5abc89b 100644
--- a/backend/app/analysis/common.py
+++ b/backend/app/analysis/common.py
@@ -64,13 +64,18 @@ def today_param(env: Env) -> str:
 def build(env: Env, params: dict[str, Any], used: Sequence[str], *, status: str = "ok",
           reason_code: str | None = None, reason: str | None = None, table: Mapping[str, Any] | None = None,
           values: Mapping[str, Any] | None = None, verdict: Mapping[str, Any] | None = None, criterion: str,
-          phrase: str, caveats: Sequence[Mapping[str, str]] = ()) -> AnalysisResult:
-    """组装一份结论：形状严格按 contract.AnalysisResult，params 补上查过的目录事实。"""
+          phrase: str, caveats: Sequence[Mapping[str, str]] = (),
+          formats: Mapping[str, str] | None = None, named: Mapping[str, str] | None = None) -> AnalysisResult:
+    """组装一份结论：形状严格按 contract.AnalysisResult，params 补上查过的目录事实。formats 是结果表各列的换算（占比、
+    变化率的列记 pct，比率的变化记 pp），named 是具名的数的换算（value_formats）：都只留记了的，为空时整项不写。"""
     record = env.facts.record()
     out_params = {k: v for k, v in params.items() if v is not None}
     if record:
         out_params["catalog"] = record
-    return {
+    out_table = dict(table) if table is not None else None
+    if out_table is not None and (table_formats := column_formats(formats or {})):
+        out_table["formats"] = table_formats
+    out: AnalysisResult = {
         "op": env.op,
         "mode": env.mode,
         "method": METHODS[(env.op, env.mode)],
@@ -81,7 +86,7 @@ def build(env: Env, params: dict[str, Any], used: Sequence[str], *, status: str
         "status": status,  # type: ignore[typeddict-item]
         "reason_code": reason_code,
         "reason": reason,
-        "table": dict(table) if table is not None else None,  # type: ignore[typeddict-item]
+        "table": out_table,  # type: ignore[typeddict-item]
         "values": dict(values or {}),
         "verdict": dict(verdict) if verdict is not None else None,  # type: ignore[typeddict-item]
         "criterion": criterion,
@@ -89,6 +94,9 @@ def build(env: Env, params: dict[str, Any], used: Sequence[str], *, status: str
         "caveats": [dict(c) for c in caveats],  # type: ignore[misc]
         "hypothesis": env.hypothesis,
     }
+    if named_formats := value_formats(named or {}):
+        out["value_formats"] = named_formats
+    return out
 
 
 def refuse(env: Env, params: dict[str, Any], used: Sequence[str], code: str, reason: str, *,
@@ -155,9 +163,19 @@ def column_types(columns: Sequence[str], *, dims: Mapping[str, str | None] | Non
     return out
 
 
+def column_formats(formats: Mapping[str, str]) -> dict[str, str]:
+    """结果表的 formats（ResultTable.formats）：列 id → 换算。只留记了换算的列，都为空时整项不写（结论和以前一字不差）。"""
+    return {str(k): v for k, v in formats.items() if v}
+
+
+def value_formats(values: Mapping[str, Any]) -> dict[str, str]:
+    """结论的 value_formats（AnalysisResult.value_formats）：键 → 换算。只留记了换算的数，都为空时整项不写。"""
+    return {str(k): v for k, v in values.items() if v}
+
+
 def sign_word(value: Fraction | float | int) -> str | None:
     return "up" if value > 0 else "down" if value < 0 else None
 
 
-__all__ = ["DERIVED_PARAMS", "Env", "build", "closes", "column_label", "column_types", "label_for", "need", "refuse",
-           "reserved_check", "sign_word", "today_param", "written_places"]
+__all__ = ["DERIVED_PARAMS", "Env", "build", "closes", "column_formats", "column_label", "column_types", "label_for",
+           "need", "refuse", "reserved_check", "sign_word", "today_param", "value_formats", "written_places"]
diff --git a/backend/app/analysis/contract.py b/backend/app/analysis/contract.py
index d8d8c61a..8f2552ac 100644
--- a/backend/app/analysis/contract.py
+++ b/backend/app/analysis/contract.py
@@ -277,6 +277,10 @@ class ResultTable(TypedDict):
     # 列 id → 类型（写法同查询快照的 column_types；第 1 波 W1-a 加，可选）：维度列照输入快照里那一列查询时记下的类型
     # （文本型的码值 "1203" 才不会被渲染成「1,203」；输入没记类型的不写），数值列 number，说明、叫法这类文字列 text
     column_types: NotRequired[dict[str, str]]
+    # 列 id → 换算（第 2 波加，可选）：占比、变化率、权重这类以比率存的列，渲染时按它换算，换算的写法同 [[m:]] 的后缀
+    # （pct 显示成百分数、pp 显示成「+1.2 个百分点」）。引用这一列的一格、展开整表时没写换算的按它渲染，写作者自己写了换算的
+    # 照他的（引用上记下实际用的换算，导出、可验证报告照它渲染）
+    formats: NotRequired[dict[str, str]]
 
 
 class AnalysisRequest(TypedDict, total=False):
@@ -302,6 +306,9 @@ class AnalysisResult(TypedDict, total=False):
     reason: str | None
     table: ResultTable | None
     values: dict[str, Any]    # 具名标量：[[a:A3.top_share]]；数一律十进制字符串
+    # 键 → 换算（第 2 波加，可选）：占比、变化率这类以比率存的数，渲染时按它换算（写法同 ResultTable.formats）。
+    # [[a:A3.top_share]] 没写换算的按它渲染，写作者自己写了换算的照他的
+    value_formats: dict[str, str]
     verdict: Verdict | None
     criterion: str            # 判定标准，一句，照实际阈值写
     phrase: str               # 判定语（app/analysis/phrases 渲染），[[a:A3]] 渲染成它
@@ -417,6 +424,16 @@ class ScopeBlock(TypedDict, total=False):
 #: 复算核对时不比的字段：hypothesis 是 agent 写的话，不是算出来的
 NOT_RECOMPUTED = frozenset({"hypothesis"})
 
+#: 结论里记格式的两处：结果表的 formats（列 id → 换算）、values 的 value_formats（键 → 换算）。换算取值同 [[m:]] 的后缀，
+#: 另加 pp（比率的变化按百分点写：「+1.2 个百分点」）
+TABLE_FORMATS = "formats"
+VALUE_FORMATS = "value_formats"
+#: 比率的换算：pct 按百分数写（0.62 显示成 62.0%）
+CONV_PCT = "pct"
+#: 比率的变化按百分点写（0.012 显示成「+1.2 个百分点」，下降时带负号）：[[m:]] 的后缀里原本没有这一种
+CONV_POINTS = "pp"
+FORMAT_CONVS: tuple[str, ...] = (CONV_PCT, CONV_POINTS)
+
 
 def same(a: Mapping[str, Any], b: Mapping[str, Any]) -> bool:
     """两份结论（封存的、复算的）是否逐字段相同（hypothesis 除外）。"""
@@ -425,8 +442,10 @@ def same(a: Mapping[str, Any], b: Mapping[str, Any]) -> bool:
 
 
 __all__ = [
-    "ARTIFACT_KIND", "CATALOG_PREFIX", "CAVEATS", "DEFAULTS", "LABELS", "LEDGER_KIND", "MARKER_KIND", "METHODS", "MODES",
-    "MODE_LABELS", "NODE_TYPE", "NOT_RECOMPUTED", "OPS", "OP_LABELS", "RANGES", "REASONS", "SETTINGS_KEY", "TOOL_NAMES",
+    "ARTIFACT_KIND", "CATALOG_PREFIX", "CAVEATS", "CONV_PCT", "CONV_POINTS", "DEFAULTS", "FORMAT_CONVS", "LABELS",
+    "LEDGER_KIND", "MARKER_KIND", "METHODS", "MODES",
+    "MODE_LABELS", "NODE_TYPE", "NOT_RECOMPUTED", "OPS", "OP_LABELS", "RANGES", "REASONS", "SETTINGS_KEY",
+    "TABLE_FORMATS", "TOOL_NAMES", "VALUE_FORMATS",
     "VERDICTS", "AnalysisInput", "AnalysisRequest", "AnalysisResult", "CallError", "Caveat", "Context", "InputSnapshot",
     "MetricComparability", "Probe", "Prober", "ResultTable", "ScopeBlock", "ScopeItem", "Verdict", "catalog_result",
     "effective_settings", "is_result", "ledger_entry", "method_label", "same", "settings_problem",
diff --git a/backend/app/analysis/decompose.py b/backend/app/analysis/decompose.py
index 104001b6..fe9f6649 100644
--- a/backend/app/analysis/decompose.py
+++ b/backend/app/analysis/decompose.py
@@ -29,7 +29,7 @@ from app.analysis import comparable as cmp
 from app.analysis import phrases as ph
 from app.analysis.common import (Env, build, closes, column_label, column_types, label_for, need, refuse,
                                  reserved_check, sign_word, today_param)
-from app.analysis.contract import CallError
+from app.analysis.contract import CONV_PCT, CONV_POINTS, CallError
 from app.analysis.inputs import Snap, as_list, hashable, same_value
 from app.analysis.numbers import canon, to_fraction
 
@@ -227,6 +227,17 @@ def _parent(raw: Mapping[str, Any], env: Env, mode: str, dims: int) -> tuple[dic
     return {"of": name, "key": key}, dict(zip(columns, hit)), depth
 
 
+#: 结果表里以比率存的列（占比、变化率、权重、比率本身）：引用、展开时按百分数显示（ResultTable.formats 记 pct）
+_RATE_COLUMNS = ("pct", "share")
+
+
+def _rate_formats(columns: Sequence[str], rate: Sequence[str] = _RATE_COLUMNS,
+                  points: Sequence[str] = ()) -> dict[str, str]:
+    """结果表各列记的换算：rate 里的列（占比、变化率、权重、比率本身）记 pct，points 里的列（比率的变化）记 pp。"""
+    return {**{c: CONV_PCT for c in rate if c in columns},
+            **{c: CONV_POINTS for c in points if c in columns}}
+
+
 _FIXED_ADDITIVE = ("base", "target", "delta", "pct", "share", "flag")
 _FIXED_RATIO = ("n_base", "n_target", "d_base", "d_target", "w_base", "w_target", "r_base", "r_target", "mix", "rate",
                 "total", "flag")
@@ -470,7 +481,9 @@ def _additive_result(env: Env, params: dict[str, Any], used: list[str], segs: li
     skip = (("offset",) if code == "flat" else ()) + (("comparability",) if outcome is None else ())
     phrase = ph.with_caveats(main, caveats, skip=skip)
     return build(env, params, used, table=table, values=values, verdict=verdict,
-                 criterion=ph.criterion_additive(s, top_k_max=k_max, summed=summed), phrase=phrase, caveats=caveats)
+                 criterion=ph.criterion_additive(s, top_k_max=k_max, summed=summed), phrase=phrase, caveats=caveats,
+                 formats=_rate_formats(columns),
+                 named={k: CONV_PCT for k in ("pct_total", "top_share", "top_k_share")})
 
 
 # --------------------------------------------------------------------------
@@ -681,7 +694,13 @@ def _ratio_result(env: Env, params: dict[str, Any], used: list[str], segs: list[
                "direction": sign_word(delta) if code in ("mix_driven", "rate_driven", "both") else None}
     return build(env, params, used, table=table, values=values, verdict=verdict, criterion=criterion,
                  phrase=ph.with_caveats(main, caveats, skip=("comparability",) if outcome is None else ()),
-                 caveats=caveats)
+                 caveats=caveats,
+                 # 权重、各段的比率按百分数显示；结构效应、自身效应、合计影响是比率的变化，按百分点
+                 formats=_rate_formats(columns, rate=("w_base", "w_target", "r_base", "r_target"),
+                                       points=("mix", "rate", "total")),
+                 named={k: CONV_PCT for k in ("r_base_total", "r_target_total")}
+                 | {k: CONV_POINTS for k in ("delta", "mix_total", "rate_total")}
+                 | {"mix_share": CONV_PCT, "rate_share": CONV_PCT})
 
 
 # --------------------------------------------------------------------------
@@ -811,7 +830,8 @@ def factor(raw: Mapping[str, Any], env: Env) -> Any:
     verdict = {"code": code, "subjects": subjects, "direction": sign_word(delta) if code != "flat" else None}
     return build(env, params, used, table=table, values=values, verdict=verdict, criterion=criterion,
                  phrase=ph.with_caveats(main, caveats, skip=("comparability",) if outcome is None else ()),
-                 caveats=caveats)
+                 caveats=caveats, formats=_rate_formats(columns),
+                 named={k: CONV_PCT for k in ("pct_total", "top_share")})
 
 
 __all__ = ["HEADLINE_SHAPE", "PARENT_SHAPE", "SAME_QUERY", "SIMPSON_SHARE", "USED", "additive", "factor", "lmdi",
diff --git a/backend/app/analysis/differ.py b/backend/app/analysis/differ.py
index 668e31e5..1b959b3b 100644
--- a/backend/app/analysis/differ.py
+++ b/backend/app/analysis/differ.py
@@ -33,7 +33,7 @@ from typing import Any
 from app.analysis import phrases as ph
 from app.analysis import stats
 from app.analysis.common import Env, build, column_label, column_types, label_for, need, refuse, sign_word
-from app.analysis.contract import CallError
+from app.analysis.contract import CONV_PCT, CONV_POINTS, CallError
 from app.analysis.inputs import Snap, as_list, hashable, same_value
 from app.analysis.numbers import canon, to_fraction
 
@@ -389,8 +389,13 @@ def _decide(env: Env, params: dict[str, Any], used: list[str], pairs: list[Pair]
               "test": shown.test, "pairs": str(len(pairs)), "differs": str(len(hits))}
     verdict = {"code": code, "subjects": subjects, "direction": direction}
     phrase = ph.with_caveats(main, caveats, skip=("multiple_comparisons",) if len(tested) > 1 else ())
+    # 比率模式的组的比率按百分数显示，两组的差、差的区间是比率之差，按百分点（均值模式是量，照原样）
+    rate = ("a_value", "b_value") if mode == "rate" else ()
+    points = ("diff", "ci_lo", "ci_hi") if mode == "rate" else ()
     return build(env, params, used, table=table, values=values, verdict=verdict, criterion=criterion, phrase=phrase,
-                 caveats=caveats)
+                 caveats=caveats, formats={**{c: CONV_PCT for c in rate}, **{c: CONV_POINTS for c in points}},
+                 named={**{k: CONV_PCT for k in ("a", "b")}, **{k: CONV_POINTS for k in ("diff", "ci_lo", "ci_hi")}}
+                 if mode == "rate" else {})
 
 
 def _sensitivity(pairs: list[Pair], mode: str, alpha: Fraction, min_expected: Fraction,
diff --git a/backend/app/analysis/series.py b/backend/app/analysis/series.py
index c9b57687..ef536014 100644
--- a/backend/app/analysis/series.py
+++ b/backend/app/analysis/series.py
@@ -33,7 +33,7 @@ from app.analysis import phrases as ph
 from app.analysis import stats
 from app.analysis.common import (Env, build, column_label, column_types, label_for, need, refuse, reserved_check,
                                  sign_word, today_param)
-from app.analysis.contract import CallError
+from app.analysis.contract import CONV_PCT, CallError
 from app.analysis.inputs import Snap, same_value
 from app.analysis.numbers import canon, to_fraction
 
@@ -258,8 +258,13 @@ def baseline(raw: Mapping[str, Any], env: Env) -> Any:
               "z": canon(z) if z is not None else None, "rank": str(stats.rank_desc(x, hv)), "n_history": str(n),
               "hist_min": canon(min(hv)), "hist_max": canon(max(hv))}
     verdict = {"code": code, "subjects": [], "direction": sign_word(x - m)}
+    # 按变化率比时，本期的变化率、中位数、常规波动范围、历史最小最大都是变化率，按百分数显示；按值本身比时照原样
+    change = ("change",) if basis == "change" else ()
     return build(env, params, used, table=table, values=values, verdict=verdict, criterion=criterion,
-                 phrase=ph.with_caveats(main, caveats, skip=("comparability",)), caveats=caveats)
+                 phrase=ph.with_caveats(main, caveats, skip=("comparability",)), caveats=caveats,
+                 formats={c: CONV_PCT for c in change},
+                 named={k: CONV_PCT for k in ("x", "median", "band_lo", "band_hi", "hist_min", "hist_max")
+                        if basis == "change"})
 
 
 def _compare_gap(series: Series, ordinal: int, lag: int, unit: str) -> str:
@@ -360,7 +365,9 @@ def trend(raw: Mapping[str, Any], env: Env) -> Any:
     verdict = {"code": code, "subjects": [], "direction": "up" if code == "rising" else "down" if code == "falling"
                else None}
     return build(env, params, used, table=table, values=values, verdict=verdict, criterion=criterion,
-                 phrase=ph.with_caveats(main, caveats, skip=("comparability",)), caveats=caveats)
+                 phrase=ph.with_caveats(main, caveats, skip=("comparability",)), caveats=caveats,
+                 # 相对斜率是比率（每期变百分之几），按百分数显示；斜率本身、p 值、各期之比照原样
+                 named={"rel_slope": CONV_PCT})
 
 
 def _tail_gap(series: Series, ordinal: int) -> str:
diff --git a/backend/app/api/evidence.py b/backend/app/api/evidence.py
index 5e6692a0..64562266 100644
--- a/backend/app/api/evidence.py
+++ b/backend/app/api/evidence.py
@@ -1188,12 +1188,17 @@ async def _analysis_chain(seg: dict[str, Any], cite: dict[str, Any], entry: dict
 
 def _analysis_calc(alias: str, result: dict[str, Any], cited: dict[str, Any], hidden: set[str]) -> dict[str, Any]:
     """计算一步：结果表（前 table_top_k 段加上被引用的那一行附近，最多 MAX_ANALYSIS_ROWS 行，高亮被引用的格）、具名的数（原值、
-    渲染出来的字）、关键数的代入式。结果表里万一有取自遮罩列的列（输入查询步骤里遮着的列名），照查询步骤写「已遮罩」。"""
+    渲染出来的字）、关键数的代入式。数和格都按结论记的格式写出来（占比、变化率是百分数，比率的变化是百分点），和正文引用
+    之后显示的字一样；结果表的空格写「—」不写 null，面板照原样显示。结果表里万一有取自遮罩列的列（输入查询步骤里遮着的
+    列名），照查询步骤写「已遮罩」。"""
     values = result.get("values") if isinstance(result.get("values"), dict) else {}
+    named = result.get("value_formats") if isinstance(result.get("value_formats"), dict) else {}
     shown_values = []
     for key, raw in values.items():
         try:
-            text = render_cell(raw)
+            # 具名的数按结论记的格式写（占比、变化率是百分数），和引用之后正文显示的字一样
+            conv = named.get(key) if isinstance(named.get(key), str) and named.get(key) else None
+            text = render_cell(raw, conv)
         except RenderError:
             text = MISSING
         shown_values.append({"key": str(key), "raw": raw, "rendered": text,
@@ -1216,8 +1221,8 @@ def _analysis_calc(alias: str, result: dict[str, Any], cited: dict[str, Any], hi
     step["table"] = {
         "columns": columns, "labels": dict(table.get("labels") or {}) if isinstance(table.get("labels"), dict) else {},
         "total_rows": len(rows), "row_index": picked,
-        "rows": [[MASKED if i in masked else v for i, v in enumerate(rows[r])] if isinstance(rows[r], list) else rows[r]
-                 for r in picked],
+        "rows": [[MASKED if i in masked else _shown_cell(v, table, columns[i]) for i, v in enumerate(rows[r])]
+                 if isinstance(rows[r], list) else rows[r] for r in picked],
         "masked": [columns[i] for i in sorted(masked)],
         "highlight": {"cells": [[row, str(cited.get("column"))]] if isinstance(row, int) else []},
         **({"window_truncated": True} if len(index) > MAX_ANALYSIS_ROWS else {}),
@@ -1225,6 +1230,22 @@ def _analysis_calc(alias: str, result: dict[str, Any], cited: dict[str, Any], hi
     return step
 
 
+def _shown_cell(raw: Any, table: Mapping[str, Any], column: str) -> str:
+    """结果表的一格按结论记的格式写出来（占比、变化率是百分数，比率的变化是百分点），空格写「—」不写 null：面板照原样显示，
+    读者看到的和正文引用那一格时一样。列类型照结果表的 column_types（文本列照原样，不按值猜）；渲染不出来（不是 0 却显示成 0）
+    的照原样写。"""
+    if raw is None:
+        return MISSING
+    formats = table.get("formats") if isinstance(table.get("formats"), dict) else {}
+    types = table.get("column_types") if isinstance(table.get("column_types"), dict) else {}
+    conv = formats.get(column) if isinstance(formats.get(column), str) and formats.get(column) else None
+    kind = types.get(column) if isinstance(types.get(column), str) else None
+    try:
+        return render_cell(raw, conv, kind=kind)
+    except RenderError:
+        return str(raw)
+
+
 async def _analysis_inputs(alias: str, result: dict[str, Any], catalog: dict[str, Any], doc: dict[str, Any],
                            sealed: _Sealed, masks: _Masks, *, queries: bool = True
                            ) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
@@ -1266,8 +1287,12 @@ async def _analysis_inputs(alias: str, result: dict[str, Any], catalog: dict[str
                     sealed=sealed.trusted and query is not None)
         if role in totals and totals[role] in values:
             with contextlib.suppress(RenderError):
-                step["closure"] = {"mode": mode, "total_key": totals[role], "total": render_cell(values[totals[role]]),
-                                   "headline": render_cell(item.get("value")), "closed": not unclosed}
+                # 整体比率那一格和对照的那一格按结论记的格式写（比率拆解里都是比率，一起写成百分数），和具名的数、正文一致
+                named = result.get("value_formats") if isinstance(result.get("value_formats"), dict) else {}
+                key = totals[role]
+                conv = named.get(key) if isinstance(named.get(key), str) and named.get(key) else None
+                step["closure"] = {"mode": mode, "total_key": key, "total": render_cell(values[key], conv),
+                                   "headline": render_cell(item.get("value"), conv), "closed": not unclosed}
         steps.append(step)
         if artifact and query is not None:
             wanted = cells.setdefault(artifact, [])
diff --git a/backend/app/bundle/verifier.js b/backend/app/bundle/verifier.js
index 0a24e27b..b89065fb 100644
--- a/backend/app/bundle/verifier.js
+++ b/backend/app/bundle/verifier.js
@@ -596,7 +596,8 @@
 
   const shifted = (d, k) => ({ neg: d.neg, coef: d.coef, exp: d.exp + k })
 
-  /** _render_numeric：万 / 亿留两位、去尾零，单位接在后面；pct、比率乘 100 加 %（不接单位）；int、.N 改小数位 */
+  /** _render_numeric：万 / 亿留两位、去尾零，单位接在后面；pct、比率乘 100 加 %（不接单位）；pp 乘 100 写成「+1.2 个百分点」；
+   * int、.N 改小数位 */
   function renderNumeric(d, unit, decimals, fmt, conv) {
     if (adjusted(d) >= MAX_DIGITS) throw new Unshown(T.shownTooLong)
     const ratio = fmt === 'percent_of_ratio'
@@ -609,6 +610,20 @@
       if (unit.includes('%') || unit.includes('％')) throw new Unshown(T.shownPctUnit)
       return `${digitsOf(shifted(d, 2), decimals != null ? Math.max(decimals - 2, 0) : 1, group, false)}%`
     }
+    if (conv === 'pp') {
+      // 比率的变化按百分点写：一位小数、正数带「+」（0 不带）；不是 0 的值写出来成了 0.0 的多给小数位（最多 4 位），
+      // 不写假的 0（同服务端 engine/evidence._render_numeric 的 pp）
+      if (unit.includes('%') || unit.includes('％')) throw new Unshown(T.shownPctUnit)
+      const points = shifted(d, 2)
+      if (isZero(points)) return `${digitsOf(points, 1, group, false)} 个百分点`
+      let places = 1
+      for (; places < 5; places++) {
+        if (!isZero(quantize(points, -places))) break
+      }
+      if (places >= 5) throw new Unshown(T.shownZero)
+      const out = digitsOf(points, places, group, false)
+      return `${out.startsWith('-') ? out : `+${out}`} 个百分点`
+    }
     let places
     if (conv === 'int') places = 0
     else if (typeof conv === 'string' && CONV_PLACES.test(conv)) places = Number(conv.slice(1))
@@ -652,12 +667,13 @@
 
   /**
    * render_cell(shown(raw))：快照里的一格。kind 是查询时记下的列类型，display 是报告文档记下的单元格显示规则版本
-   * （2 版起 ISO 日期时间的 T 换成空格；3 版起标识列的数不分千位）。plain 是引用上记下的「这一格按标识列显示」：
+   * （2 版起 ISO 日期时间的 T 换成空格；3 版起标识列的数不分千位；4 版起引用分析结论时按结论记的格式换算）。
+   * plain 是引用上记下的「这一格按标识列显示」：
    * 哪几列是标识列服务端按 SQL 和冻结的数据目录判（cell_format.plain_columns），本页解析不了 SQL，照引用上记的渲染——
    * 它只决定分不分千位，值本身照样逐位比对。DECIMAL 落成的数字文本按数渲染、不经 float；文字压成一行，竖线换成 ¦
    */
   function renderCell(raw, token, conv, kind, display, plain = false) {
-    if (display !== 1 && display !== 2 && display !== 3) throw new Unshown(T.shownDisplay(display))
+    if (display !== 1 && display !== 2 && display !== 3 && display !== 4) throw new Unshown(T.shownDisplay(display))
     if (!PY_NUMERIC || !ISO_DATETIME) throw new Unshown(T.shownOld)
     const fmt = display >= 3 && plain === true ? 'plain' : 'thousands'
     let value = raw
@@ -1556,6 +1572,12 @@
   /** 引用上记的换算（|万、|pct 这类），没写是 null */
   const convOf = (cite) => (own(cite, 'conv') === undefined ? null : cite.conv)
 
+  /** 分析结论给这一列 / 这个数记的换算（formats / value_formats：占比、变化率的列 pct，比率的变化 pp），没记是 null */
+  const namedConv = (formats, key) => {
+    const found = isObj(formats) ? formats[key] : null
+    return typeof found === 'string' && found ? found : null
+  }
+
   /**
    * 分析结论的一处引用（checkNumber 已核过工件的哈希和引用链）：引用的值等于结论里记的那个值，正文显示的字等于按服务端
    * 的规则渲染出来的字。判定语按文本格渲染（engine/evidence.render_cell 的 kind=text：压成一行、竖线换成 ¦），具名的数按
@@ -1586,7 +1608,10 @@
       }
       if (!sameValue(cite.value, info.truth)) return { ...info, result: FAIL, reason: T.numAnalysisCell }
       const display = Object.prototype.hasOwnProperty.call(doc, 'cell_display') ? doc.cell_display : 1
-      return checkShown(info, seg, () => renderCell(cell.raw, info.truthToken, convOf(cite),
+      // 没写换算、结论的结果表给这一列记了格式（formats：4 版显示规则起）、文档也按 4 版复核的，按它渲染（服务端同样）；
+      // 写作者自己写了换算的照他的
+      const conv = convOf(cite) ?? (display >= 4 ? namedConv(table.formats, cell.column) : null)
+      return checkShown(info, seg, () => renderCell(cell.raw, info.truthToken, conv,
         columnKind(table, cell.column), display, own(cite, 'plain') === true))
     }
     if (loc.key != null) {
@@ -1597,7 +1622,10 @@
       info.truthToken = tokenAt(tokensOf(art), ['values', loc.key])
       if (typeof raw === 'string' && typeof info.truth === 'number') info.truthText = pyStrip(raw)
       if (!sameValue(cite.value, info.truth)) return { ...info, result: FAIL, reason: T.numValueDiffers }
-      return checkShown(info, seg, () => renderCell(raw, info.truthToken, convOf(cite), null, 1))
+      // 没写换算、结论给这个数记了格式（value_formats；4 版显示规则起）的按它渲染（服务端同样）；写作者自己写了换算的照他的
+      const display = Object.prototype.hasOwnProperty.call(doc, 'cell_display') ? doc.cell_display : 1
+      const conv = convOf(cite) ?? (display >= 4 ? namedConv(result.value_formats, loc.key) : null)
+      return checkShown(info, seg, () => renderCell(raw, info.truthToken, conv, null, 1))
     }
     if (typeof result.phrase !== 'string' || !result.phrase.trim()) return { ...info, result: FAIL, reason: T.numNoPhrase }
     info.truth = result.phrase
diff --git a/backend/app/engine/evidence.py b/backend/app/engine/evidence.py
index 6fd87d63..3de76df6 100644
--- a/backend/app/engine/evidence.py
+++ b/backend/app/engine/evidence.py
@@ -91,11 +91,15 @@ ENTITY_SYNTAXES = frozenset({1, 2})
 #:   「没有第 N 行」重新解析得了，会被当成文档被改过（render_mismatch）。另外标识列（编码、编号、分组键，见
 #:   cell_format.plain_columns）的数不分千位：编码 1203 不写成「1,203」。真有格子因此改了写法的文档记 cell_display: 3，
 #:   引用上记 plain: true（离线核对页解析不了 SQL，照它渲染）
+#: - 4：引用分析结论的结果表一格、具名的数时没写换算的，按结论给这一列 / 这个数记的格式渲染（占比、变化率按百分数，
+#:   比率的变化按百分点：0.62 显示成 62.0%，不再照原样写 0.618556701），实际用的换算记在引用上（conv，导出页和可验证报告
+#:   照它渲染）。3 版及以前组装的文档里的引用照当年原样显示，复核按记下的版本，不因此判对不上
 #: 新组装的文档里真有格子因为显示规则改了写法时才记 cell_display（参与内容哈希），记的是渲染得出这些格子的最低一版
-#: （只有日期时间被改写的记 2：标识列没有因此改写法的，3 版的整表截行不改格子的写法），没有的文档和以前一字不差。复核必须按
-#: 文档生成时的那一版：老文档存的是带 T 的字，拿新规则重新渲染对不上，会被当成文档被改过（render_mismatch）
-CELL_DISPLAY = 3
-CELL_DISPLAYS = frozenset({1, 2, 3})
+#: （只有日期时间被改写的记 2：标识列没有因此改写法的，3 版的整表截行不改格子的写法，引用分析结论时本来就有换算的记 1）；没有的
+#: 文档和以前一字不差。复核必须按文档生成时的那一版：老文档存的是带 T 的字，拿新规则重新渲染对不上，会被当成文档被改过
+#: （render_mismatch）
+CELL_DISPLAY = 4
+CELL_DISPLAYS = frozenset({1, 2, 3, 4})
 _ISO_DATETIME = re.compile(r"^(\d{4}-\d{2}-\d{2})T(\d{2}:\d{2}(?::\d{2}(?:\.\d+)?)?(?:Z|[+-]\d{2}:?\d{2})?)$")
 
 
@@ -148,7 +152,7 @@ class Citation(TypedDict, total=False):
     status: Literal["resolved", "unresolved", "mismatch"]
     reason: str               # unresolved 时为什么
     unknown: bool             # 实体：本次运行哪里都没有这个名字（可疑实体，不是写法错了）
-    conv: str                 # 换算：万 / 亿 / pct / int / .N
+    conv: str                 # 换算：万 / 亿 / pct / pp / int / .N（没写换算、按分析结论记的格式渲染的也照记，导出、可验证报告照它渲染）
     value: Any
     rendered: str
     source: dict[str, Any]    # 引文：原文所在的检索快照、文档、片段
@@ -573,6 +577,11 @@ MISSING = "—"
 
 _CONV_SCALE = {"万": Decimal(10) ** 4, "亿": Decimal(10) ** 8}
 _CONV_PLACES = re.compile(r"^\.([0-6])$")
+#: 比率的变化按百分点写（0.012 →「+1.2 个百分点」，-0.003 →「-0.3 个百分点」）：分析结论的结果表、具名的数里的换算，
+#: 算子记成 pp（analysis.contract.CONV_POINTS）。正数带「+」（和 pct 一样），不是 0 的值写出来成了 0.0 的多给小数位
+#: （最多 4 位），不写假的 0
+_CONV_POINTS = "pp"
+_POINTS_SUFFIX = " 个百分点"
 #: 小数位最多写到多少。负数是取整到十位、百位……（round(x, -2) 的意思）
 MAX_PLACES = 100
 #: 整数部分最多多少位。浮点数到头也就 309 位，再大的整数按规则一位位写出来没有意义
@@ -648,7 +657,7 @@ def render_number(
     - decimals：固定小数位；不写就按值本来的样子（整数不带小数点）
     - 单位直接接在数字后面：45,678.5元、8.7%
     - conv 是报告里写的换算：万 / 亿（保留两位、去尾零，单位接在后面：4.57万元）、
-      pct（比率 ×100 加 %）、int（取整）、.N（N 位小数）
+      pct（比率 ×100 加 %）、pp（比率的变化 ×100 写成「+1.2 个百分点」）、int（取整）、.N（N 位小数）
 
     值是 None 显示「—」。文本值原样（压成一行），不能换算。
 
@@ -696,13 +705,30 @@ def _render_numeric(d: Decimal, *, unit: str, decimals: int | None, fmt: str, co
             raise RenderError("该指标本身是百分数（单位为 %），不能再换算为 pct")
         places = max(decimals - 2, 0) if decimals is not None else 1
         return _digits(_ctx(d).multiply(d, 100), places, group=group, zero=rounded) + "%"
+    if conv == _CONV_POINTS:
+        # 比率的变化按百分点写：一位小数、正数带「+」（0 不带）；不是 0 的值写出来成了 0.0 的多给小数位（最多 4 位），
+        # 不写假的 0。说法照 phrases.points / ratio_change 里已有的百分点写法，只是表格里的数没有「上升 / 下降」可依，
+        # 正负靠符号
+        if "%" in unit or "％" in unit:
+            raise RenderError("该指标本身是百分数（单位为 %），不能再换算为 pp")
+        points = _ctx(d).multiply(d, 100)
+        if points.is_zero():
+            return _digits(points, 1, group=group) + _POINTS_SUFFIX
+        zero = f"{shown} 按 |pp 显示为 0，精度不够：请去掉换算，或改用 |.N 多保留几位小数"
+        for places in range(1, 5):
+            exp = -(places)
+            if points.quantize(Decimal(1).scaleb(exp), context=_ctx(points, places)).is_zero():
+                continue
+            out = _digits(points, places, group=group, zero=zero)
+            return ("+" + out if not out.startswith("-") else out) + _POINTS_SUFFIX
+        raise RenderError(zero)
 
     if conv == "int":
         places: int | None = 0
     elif conv and (m := _CONV_PLACES.match(conv)):
         places = int(m.group(1))
     elif conv:
-        raise RenderError(f"无法识别的换算 |{conv}，只支持 万 / 亿 / pct / int / .N（N 为 0–6）")
+        raise RenderError(f"无法识别的换算 |{conv}，只支持 万 / 亿 / pct / pp / int / .N（N 为 0–6）")
     else:
         rounded = (f"{shown} 按口径卡的小数位（{decimals}）显示为 0，精度不够：请在口径卡中增加小数位"
                    if decimals is not None else
@@ -1468,8 +1494,8 @@ class _Snapshots:
 
     def table(self, alias: str, entry: dict[str, Any]) -> tuple[Any, str | None]:
         """按单元格、整表取的那张表：(表, 取不到的原因)。查询结果是查询快照本身；分析结论是它的结果表（形状同查询快照：
-        columns 是稳定的列 id，另带 labels 给人看的列名，整表的表头写它）。结果表另记了列类型（column_types）的照着渲染，
-        没记的按值认（同老快照）。"""
+        columns 是稳定的列 id，另带 labels 给人看的列名，整表的表头写它）。结果表另记了列类型（column_types）、各列的换算
+        （formats，占比、变化率这类以比率存的列）的照着渲染，没记的按值认（同老快照）。"""
         if entry.get("kind") != LEDGER_KIND:
             return self.get(alias, entry.get("artifact"))
         result, why = self.analysis(alias, entry)
@@ -1480,9 +1506,11 @@ class _Snapshots:
             return None, f"{alias} 没有结果表" + ("（这次分析没有做成）" if result.get("status") == "refused" else "")
         labels = table.get("labels")
         types = table.get("column_types")
+        formats = table.get("formats")
         return {"columns": [str(c) for c in table.get("columns") or []], "rows": table["rows"],
                 "labels": {str(k): str(v) for k, v in labels.items()} if isinstance(labels, dict) else {},
-                **({"column_types": types} if isinstance(types, dict) else {})}, None
+                **({"column_types": types} if isinstance(types, dict) else {}),
+                **({"formats": {str(k): str(v) for k, v in formats.items()}} if isinstance(formats, dict) else {})}, None
 
     def hits(self, alias: str, artifact: Any) -> tuple[list[Any], str | None]:
         """检索快照里的命中：(命中列表, 取不到的原因)。"""
@@ -1569,6 +1597,19 @@ def _noun(entry: dict[str, Any] | None) -> str:
     return "结果表" if (entry or {}).get("kind") == LEDGER_KIND else "查询结果"
 
 
+def _column_conv(snapshot: Any, column: str, display: int = CELL_DISPLAY) -> str | None:
+    """分析结论的结果表给这一列记的换算（ResultTable.formats：占比、变化率的列记 pct，比率的变化记 pp）。查询快照没有这一项，
+    一切照原样（None）。按分析结论记的格式显示是 4 版起的显示规则：老文档（3 版及以前）里的引用照当年原样显示，复核不因此
+    判对不上。"""
+    if display < 4:
+        return None
+    formats = snapshot.get("formats") if isinstance(snapshot, dict) else None
+    if not isinstance(formats, dict):
+        return None
+    found = formats.get(column)
+    return found if isinstance(found, str) and found else None
+
+
 def _resolve_cell(marker: dict[str, Any], catalog: dict[str, Any], snaps: _Snapshots,
                   cells_allowed: bool) -> dict[str, Any]:
     ref = marker.get("ref") or ""
@@ -1596,7 +1637,14 @@ def _resolve_cell(marker: dict[str, Any], catalog: dict[str, Any], snaps: _Snaps
         raw, column = locate_cell(snapshot, parsed["row"], parsed["column"])
         kind = column_kind(snapshot, column)
         shown = snaps.shown(raw)
+        # 分析结论的结果表记了这一列的换算（占比、变化率这类以比率存的列；4 版显示规则起）、写作者没写换算的，按它渲染；
+        # 写作者自己写了换算的照他的。实际用的换算记进引用（导出页、可验证报告照它渲染）
+        conv = conv or _column_conv(snapshot, column, snaps.display)
         rendered = render_cell(shown, conv, kind=kind)
+        if conv and not marker.get("conv") and rendered != render_cell(shown, None, kind=kind):
+            # 按分析结论记的格式渲染、真改了写法（4 版起）：文档的 cell_display 记成 4，引用上记 conv，
+            # 导出页和可验证报告照引用上的换算渲染
+            snaps.needed = max(snaps.needed, 4)
         plain = column in snaps.plain(entry.get("artifact"), snapshot) \
             and render_cell(shown, conv, kind=kind, plain=True) != rendered
         if plain:
@@ -1840,6 +1888,7 @@ def _resolve_analysis(marker: dict[str, Any], catalog: dict[str, Any], snaps: _S
                       cells_allowed: bool) -> dict[str, Any]:
     """[[a:A3]] → 判定语：分析结论里系统按模板渲染好的那一整句（判定是平台的算子算的，写作者只引用，00-总方案 D5）。
     [[a:A3.top_share|pct]] → 具名标量：结论 values 里的一项（数一律十进制字符串），按单元格的规则渲染，换算同 [[m:]]。
+    没写换算、结论给这个数记了格式（value_formats）的按它渲染（0.62 显示成 62.0%，不是 0.62），实际用的换算记进引用。
 
     判定语是一个片段（种类 analysis），里面的数和字都是系统写的：组装、复核都按带引用的片段整段涂白，不算裸数字。受管级别的
     正式出具、契约没开「单元格引用」时，Agent 现场做的分析解析不了（ANALYSIS_CELLS_REASON），分析卡做的照常解析。"""
@@ -1879,6 +1928,13 @@ def _resolve_analysis(marker: dict[str, Any], catalog: dict[str, Any], snaps: _S
         return _unresolved(marker, alias, LEDGER_KIND, _Reason(
             f"{alias} 中没有名为「{key}」的数", f"{alias} 里没有「{key}」，能引用的数：{names}（写成 [[{MARKER_KIND}:{alias}.键]]）"))
     raw = values[key]
+    # 结论里给这个数记了换算（value_formats：占比、变化率这类以比率存的数；4 版显示规则起）而写作者没写换算的，按它渲染；
+    # 写作者自己写了换算的照他的。实际用的换算记进引用（导出页、可验证报告照它渲染）
+    named = result.get("value_formats") if isinstance(result.get("value_formats"), dict) else {}
+    if snaps.display >= 4 and not conv and isinstance(named.get(key), str) and named.get(key):
+        conv = named[key]
+        # 按分析结论记的格式渲染了（4 版起）：文档记成 4，引用上记 conv（写了换算才真有换算，写法一定和以前不同）
+        snaps.needed = max(snaps.needed, 4)
     try:
         rendered = render_cell(raw, conv)
     except RenderError as e:
@@ -4032,7 +4088,8 @@ def _query_prompt(entry: dict[str, Any], snaps: _Snapshots, cells_allowed: bool,
 ANALYSIS_PROMPT = (
     "分析结论（系统按平台的方法算好的判定。判定语、具名的数、结果表都只能用引用标记写，系统照结论渲染、逐字核对）：\n"
     "写法：判定语单独写 [[a:A1]]，系统换成整句判定；具名的数写 [[a:A1.键]]，换算同指标，比重、变化率这类比率写成 "
-    "[[a:A1.top_share|pct]] 显示成百分数；结果表的一格写 [[v:A1.r0.列]]（行号从 0 数），要列出多行多列，单独一行写 "
+    "[[a:A1.top_share|pct]] 显示成百分数；比重、变化率不写换算也按百分数显示，比率的变化按百分点（[[v:A1.r0.mix]] 显示成「1.2 "
+    "个百分点」）；结果表的一格写 [[v:A1.r0.列]]（行号从 0 数），要列出多行多列，单独一行写 "
     "[[table:A1 cols=列a,列b rows=0-4]]；拿它当结论的依据，句末写 [[see:A1]]。"
     "「主要来自」「超出常规波动」「呈上升趋势」这类判断已经写在判定语里：引用判定语，不要自己改写判定；"
     "算不了的照实写算不了的原因。下面没有的编号、键、列不要编造。"
@@ -4105,10 +4162,13 @@ def _analysis_prompt(entry: dict[str, Any], catalog: dict[str, Any], snaps: _Sna
     if see_only:
         return lines
     values = result.get("values") if isinstance(result.get("values"), dict) else {}
+    named = result.get("value_formats") if isinstance(result.get("value_formats"), dict) else {}
     shown = []
     for key, raw in list(values.items())[:_PROMPT_VALUES]:
         try:
-            shown.append(f"{key}={render_cell(raw)}")
+            # 具名的数按结论记的格式写（占比、变化率是百分数）：写作者在这里看到的就是引用之后显示的字
+            conv = named.get(key) if isinstance(named.get(key), str) and named.get(key) else None
+            shown.append(f"{key}={render_cell(raw, conv)}")
         except RenderError:
             shown.append(f"{key}={MISSING}")
     if shown:
@@ -4131,12 +4191,15 @@ def _analysis_prompt(entry: dict[str, Any], catalog: dict[str, Any], snaps: _Sna
     top = top if isinstance(top, int) and not isinstance(top, bool) and top > 0 else _TOP_K
     lines.append(f"  结果表（共 {len(rows)} 段）：列 {named}")
     plain = snaps.plain(entry.get("artifact"), snapshot)
+    formats = snapshot.get("formats") if isinstance(snapshot.get("formats"), dict) else {}
     for r in range(min(top, len(rows))):
         cells = []
         for c in columns:
             try:
                 raw, _ = locate_cell(snapshot, r, c)
-                cells.append(f"{c}={render_cell(snaps.shown(raw), kind=column_kind(snapshot, c), plain=c in plain)}")
+                conv = formats.get(c) if isinstance(formats.get(c), str) and formats.get(c) else None
+                cells.append(f"{c}={render_cell(snaps.shown(raw), conv, kind=column_kind(snapshot, c),
+                                                plain=c in plain)}")
             except (CellError, RenderError):
                 cells.append(f"{c}={MISSING}")
         lines.append(f"  r{r}：" + "，".join(cells))
diff --git a/backend/tests/test_analysis_conclusion_formats.py b/backend/tests/test_analysis_conclusion_formats.py
new file mode 100644
index 00000000..95198f83
--- /dev/null
+++ b/backend/tests/test_analysis_conclusion_formats.py
@@ -0,0 +1,357 @@
+"""分析结论里的占比、变化率、权重按原始小数显示（第 2 波复核 S1，显示规则 4 版）。
+
+复核实测：结果表里占变化的比重、变化率以比率存（0.618556701031），正文引用 [[a:A1.top_share]]、[[v:A1.r0.share]]、
+[[table:A1 cols=region,pct,share]] 时照原样写「占比 3.5190412373」；判定语写「占 351.9%」，正文写「占比 3.5190412373」，
+读的人把 0.62 当成 62% 的百分之一。现在算子给结果表的、以比率存的列记换算（ResultTable.formats：占比、变化率、权重记 pct，
+比率的变化记 pp），结论的 values 里同样记一份（value_formats）；引用、整表、写作目录、证据面板、导出、可验证报告都按它渲染，
+实际用的换算记在引用上（导出页、可验证报告照它渲染）。写作者自己写了换算的照他的。3 版及以前组装的文档照当年原样显示，
+复核不因此判对不上。
+
+数据全是虚构的（华东、华南、华北、西南，销售额、订单数，虚构零售库 shop）。
+"""
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import pytest
+from httpx import ASGITransport, AsyncClient
+
+from app.analysis.contract import CONV_PCT, CONV_POINTS, ledger_entry
+from app.core import artifact_store
+from app.engine import evidence as ev
+from app.engine.evidence import build_catalog, compose_doc, iter_segments, render_cell, resolve_ref, verify_doc, \
+    writer_catalog
+from app.main import app as asgi
+from tests.analysis_samples import analyze, snap
+from tests.test_analysis_bundle import Files, GRAPH, RUN, RUN_ID, bundle_of, item, verdicts
+from tests.test_analysis_evidence import SNAPSHOT, Store, real_result
+from tests.test_analysis_evidence_api import seg_id, sealed_run
+
+WEEKS = [["华东", "2026-W41", 820], ["华东", "2026-W42", 700], ["华北", "2026-W41", 600], ["华北", "2026-W42", 560],
+         ["华南", "2026-W41", 700], ["华南", "2026-W42", 666], ["西南", "2026-W41", 100], ["西南", "2026-W42", 100]]
+REGIONS = snap("Q2", "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week",
+               ["region", "week", "gmv"], WEEKS, types={"region": "text", "week": "text", "gmv": "number"})
+
+
+def run_sync(coro: Any) -> Any:
+    from tests.test_analysis_evidence import run_sync as wait
+
+    return wait(coro)
+
+
+def analyzed(op: str, mode: str | None, **params: Any) -> dict[str, Any]:
+    """真算一份结论（同 tests/analysis_samples.analyze：跑一次、复算一遍，逐字段相同）。"""
+    return run_sync(analyze(op, mode, snaps=[REGIONS], query="Q2", **params))
+
+
+def catalog_of(*, result: dict[str, Any] | None = None) -> tuple[Store, dict[str, Any]]:
+    """一次查询（Q1）加一份真算的按地区拆的分析结论（A1）：(工件库, 写作目录)。结论带算子记的格式。"""
+    store = Store()
+    q = store.put(SNAPSHOT)
+    res = result or real_result("Q2")
+    a = store.put(res)
+    ledger = [{"kind": "query", "node_id": "fetch", "exec": 1, "artifact": q, "tool": "db_query__shop",
+               "source": "shop", "columns": ["region", "week", "gmv"], "rows": 8, "truncated": False},
+              ledger_entry(res, node_id="an", exec_no=1, artifact=a, item="drop")]
+    return store, build_catalog(nodes={}, ledger=ledger, loader=store.load)
+
+
+# --------------------------------------------------------------------------
+# 换算的写法：pct、pp
+# --------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize("value, conv, shown", [
+    ("0.618556701", "pct", "61.9%"),
+    ("3.51904123734", "pct", "351.9%"),
+    ("-0.0142977693", "pct", "-1.4%"),
+    ("0.0123", "pp", "+1.2 个百分点"),
+    ("-0.121944594212", "pp", "-12.2 个百分点"),
+    ("0", "pp", "0.0 个百分点"),
+    ("0.00004", "pp", "+0.004 个百分点"),           # 不是 0 的不写成 0.0
+])
+def test_pct_and_percentage_points_are_written_the_way_the_verdict_phrase_writes_them(value, conv, shown):
+    """pct 和 phrases.pct 一个写法（一位小数、不带正号）；pp 是百分点（一位小数，不是 0 的 0.0 多给小数位），
+    和判定语里「上升 0.4 个百分点」同一个说法。"""
+    assert render_cell(value, conv) == shown
+
+
+def test_a_conversion_that_shows_a_nonzero_value_as_zero_still_fails():
+    with pytest.raises(ev.RenderError, match="显示为 0"):
+        render_cell("0.0000001", "pp")
+
+
+# --------------------------------------------------------------------------
+# 各算子记的格式
+# --------------------------------------------------------------------------
+
+
+def test_the_additive_decomposition_formats_shares_and_rates():
+    result = analyzed("decompose", "additive", by=["region"], period="week", base="2026-W41", target="2026-W42",
+                      measure="gmv", label="销售额")
+    assert result["table"]["formats"] == {"pct": CONV_PCT, "share": CONV_PCT}
+    assert result["value_formats"] == {"pct_total": CONV_PCT, "top_share": CONV_PCT, "top_k_share": CONV_PCT}
+    # 维度列、量（基期、本期、变化）没有换算
+    assert set(result["table"]["formats"]) & {"region", "base", "target", "delta"} == set()
+
+
+def test_the_ratio_decomposition_formats_weights_as_percent_and_effects_as_points():
+    rows = [["华东", "2026-W41", 8, 100], ["华东", "2026-W42", 10, 120], ["华南", "2026-W41", 5, 130],
+            ["华南", "2026-W42", 4, 140]]
+    rates = snap("Q3", "SELECT region, week, refunds, orders FROM refunds GROUP BY region, week",
+                 ["region", "week", "refunds", "orders"], rows,
+                 types={"region": "text", "week": "text", "refunds": "number", "orders": "number"})
+    result = run_sync(analyze("decompose", "ratio", snaps=[rates], query="Q3", by=["region"], period="week",
+                              base="2026-W41", target="2026-W42", numerator="refunds", denominator="orders",
+                              label="退款率"))
+    # 权重、各段的比率是比率本身 → pct；结构效应、自身效应、合计影响是比率的变化 → pp
+    assert result["table"]["formats"] == {"w_base": CONV_PCT, "w_target": CONV_PCT, "r_base": CONV_PCT,
+                                          "r_target": CONV_PCT, "mix": CONV_POINTS, "rate": CONV_POINTS,
+                                          "total": CONV_POINTS}
+    assert result["value_formats"]["r_base_total"] == CONV_PCT and result["value_formats"]["r_target_total"] == CONV_PCT
+    assert {result["value_formats"][k] for k in ("delta", "mix_total", "rate_total")} == {CONV_POINTS}
+    assert result["value_formats"]["mix_share"] == CONV_PCT
+
+
+def test_the_factor_decomposition_formats_change_rates_and_shares():
+    product = snap("Q4", "SELECT week, gmv, users, price FROM orders", ["week", "gmv", "users", "price"],
+                   [["2026-W41", 1000, 100, 10], ["2026-W42", 1188, 110, 10.8]],
+                   types={"week": "text", "gmv": "number", "users": "number", "price": "number"})
+    result = run_sync(analyze("decompose", "factor", snaps=[product], query="Q4", period="week", base="2026-W41",
+                              target="2026-W42", product="gmv", factors=["users", "price"], label="销售额"))
+    assert result["table"]["formats"] == {"pct": CONV_PCT, "share": CONV_PCT}
+    assert result["value_formats"] == {"pct_total": CONV_PCT, "top_share": CONV_PCT}
+
+
+def test_the_baseline_formats_change_rates_only_when_it_compares_changes():
+    weekly = snap("Q1", "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week", ["week", "gmv"],
+                  [[f"2026-W{k:02d}", 2200 + (k % 5) * 10] for k in range(30, 42)] + [["2026-W42", 2138]],
+                  types={"week": "text", "gmv": "number"})
+    changes = run_sync(analyze("baseline", None, snaps=[weekly], query="Q1", period="week", measure="gmv",
+                               label="销售额", grain="week"))
+    assert changes["table"]["formats"] == {"change": CONV_PCT}
+    assert {"x", "median", "band_lo", "band_hi"} <= set(changes["value_formats"])
+    levels = run_sync(analyze("baseline", None, snaps=[weekly], query="Q1", period="week", measure="gmv",
+                              label="销售额", grain="week", basis="level"))
+    # 按值本身比时是量，照原样显示（没有换算）
+    assert "formats" not in levels["table"] and "value_formats" not in levels
+
+
+def test_the_trend_formats_the_relative_slope():
+    weekly = snap("Q1", "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week", ["week", "gmv"],
+                  [[f"2026-W{k:02d}", 100 + k] for k in range(30, 42)], types={"week": "text", "gmv": "number"})
+    result = run_sync(analyze("trend", None, snaps=[weekly], query="Q1", period="week", measure="gmv",
+                              label="销售额", grain="week"))
+    assert result["value_formats"] == {"rel_slope": CONV_PCT}
+
+
+def test_the_differ_formats_rates_as_percent_and_differences_as_points():
+    groups = snap("Q3", "SELECT region, refunds, orders FROM refunds GROUP BY region", ["region", "refunds", "orders"],
+                  [["华东", 100, 2000], ["华南", 120, 1800], ["华北", 90, 2100], ["西南", 40, 1900]],
+                  types={"region": "text", "refunds": "number", "orders": "number"})
+    result = run_sync(analyze("differ", "rate", snaps=[groups], query="Q3", by=["region"], numerator="refunds",
+                              denominator="orders", label="退款率"))
+    assert result["table"]["formats"] == {"a_value": CONV_PCT, "b_value": CONV_PCT, "diff": CONV_POINTS,
+                                          "ci_lo": CONV_POINTS, "ci_hi": CONV_POINTS}
+    assert {result["value_formats"][k] for k in ("a", "b")} == {CONV_PCT}
+    assert {result["value_formats"][k] for k in ("diff", "ci_lo", "ci_hi")} == {CONV_POINTS}
+
+
+def test_a_run_twice_is_byte_for_byte_the_same_and_recompute_matches():
+    """格式是结论的一部分：同样的输入两次 run 逐字节相同，复算和封存的逐字段相同（analysis_samples.analyze 顺带核对复算）。"""
+    first = analyzed("decompose", "additive", by=["region"], period="week", base="2026-W41", target="2026-W42",
+                     measure="gmv", label="销售额")
+    second = analyzed("decompose", "additive", by=["region"], period="week", base="2026-W41", target="2026-W42",
+                      measure="gmv", label="销售额")
+    assert artifact_store.canonical_json(first) == artifact_store.canonical_json(second)
+    assert first["table"]["formats"] == second["table"]["formats"]
+
+
+# --------------------------------------------------------------------------
+# 引用：没写换算时取列 / 数的格式，写了照写作者的
+# --------------------------------------------------------------------------
+
+
+def test_citing_a_share_or_rate_without_a_conversion_uses_the_conclusion_format():
+    store, catalog = catalog_of()
+    doc = compose_doc("华东占 [[a:A1.top_share]]，变化率 [[v:A1.r0.pct]]，占比 [[v:A1.r0.share]]，"
+                      "变化量 [[v:A1.r0.delta]]。[[see:A1]]", catalog, loader=store.load)
+    shown = {s["ref"]: s["text"] for s in iter_segments(doc) if s.get("ref")}
+    assert shown["a:A1.top_share"] == "61.9%"
+    assert shown["v:A1.r0.pct"] == "-14.6%" and shown["v:A1.r0.share"] == "61.9%"
+    assert shown["v:A1.r0.delta"] == "-120"        # 量照原样
+    cites = {s["ref"]: s["cite"] for s in iter_segments(doc) if s.get("ref")}
+    # 实际用的换算记在引用上（导出页、可验证报告照它渲染）
+    assert cites["a:A1.top_share"]["conv"] == "pct" and cites["v:A1.r0.share"]["conv"] == "pct"
+    assert "conv" not in cites["v:A1.r0.delta"]
+    assert doc["cell_display"] == 4 and doc["violations"] == []
+    assert verify_doc(doc, catalog, loader=store.load)["ok"]
+
+
+def test_a_writer_supplied_conversion_wins_over_the_conclusion_format():
+    store, catalog = catalog_of()
+    cite = resolve_ref("a:A1.top_share|.2", catalog, loader=store.load)
+    assert cite["rendered"] == "0.62" and cite["conv"] == ".2"
+    cell = resolve_ref("v:A1.r0.share|pct", catalog, loader=store.load)
+    assert cell["rendered"] == "61.9%"
+
+
+def test_a_whole_table_is_expanded_with_the_conclusion_formats():
+    store, catalog = catalog_of()
+    doc = compose_doc("[[table:A1 cols=region,delta,pct,share rows=0-1]]", catalog, loader=store.load)
+    assert "| region | 变化 | 变化率 | 占变化的比重 |" in doc["markdown"]
+    assert "| 华东 | -120 | -14.6% | 61.9% |" in doc["markdown"]
+    assert "| 华北 | -40 | -6.7% | 20.6% |" in doc["markdown"]
+    assert verify_doc(doc, catalog, loader=store.load)["ok"]
+
+
+def test_an_old_document_keeps_the_raw_decimals_and_still_verifies(monkeypatch):
+    """3 版及以前组装的文档：引用上是 0.618556701 这样的原样，复核按记下的版本渲染，不判对不上；按新规则重渲染会判对不上。"""
+    store, catalog = catalog_of()
+    monkeypatch.setattr(ev, "CELL_DISPLAY", 3)
+    old = compose_doc("占比 [[a:A1.top_share]]，[[v:A1.r0.share]]。[[see:A1]]", catalog, loader=store.load)
+    monkeypatch.undo()
+    assert "cell_display" not in old
+    shown = {s["ref"]: s["text"] for s in iter_segments(old) if s.get("ref")}
+    assert shown["a:A1.top_share"] == "0.618556701" and shown["v:A1.r0.share"] == "0.618556701"
+    assert verify_doc(old, catalog, loader=store.load)["ok"]
+    # 拿 4 版复核这份老文档：引用上的字还是原样，照样对得上（引用上没记换算，按引用上的字渲染）
+    assert {s["ref"]: s["cite"].get("conv") for s in iter_segments(old) if s.get("ref")} == \
+        {"a:A1.top_share": None, "v:A1.r0.share": None}
+    # 反过来：4 版组装的文档按 3 版复核，引用上的字是 61.9%，老规则渲染出来是原样，判对不上
+    now = compose_doc("占比 [[a:A1.top_share]]。[[see:A1]]", catalog, loader=store.load)
+    mismatched = verify_doc({**now, "cell_display": 3}, catalog, loader=store.load)
+    assert mismatched["ok"] is False
+    assert [v["code"] for v in mismatched["violations"]] == ["render_mismatch"]
+
+
+# --------------------------------------------------------------------------
+# 写作目录
+# --------------------------------------------------------------------------
+
+
+def test_the_writer_catalog_shows_the_shares_as_percentages():
+    store, catalog = catalog_of()
+    text = writer_catalog(catalog, loader=store.load).text
+    section = text[text.index("分析结论（"):text.index("查询结果（")]
+    assert "top_share=61.9%" in section and "top_share=0.618" not in section
+    assert "r0：region=华东，base=820，target=700，delta=-120，pct=-14.6%，share=61.9%" in section
+    assert "份额、变化率不写换算也按百分数显示" in section or "不写换算也按百分数显示" in section
+
+
+# --------------------------------------------------------------------------
+# 证据面板接口
+# --------------------------------------------------------------------------
+
+
+@pytest.fixture
+async def client():
+    async with AsyncClient(transport=ASGITransport(app=asgi), base_url="http://test") as c:
+        yield c
+
+
+async def test_the_panel_shows_the_named_value_and_the_table_cells_as_written(client):
+    made = await sealed_run("[[a:A1]]。占比 [[a:A1.top_share]]，[[v:A1.r0.share]]。[[see:A1]]")
+    body = (await client.get(
+        f"/api/runs/{made['run']}/evidence/segments/{seg_id(made['doc'], 'a:A1.top_share')}")).json()
+    calc = body["chain"][2]
+    top = next(v for v in calc["values"] if v["key"] == "top_share")
+    assert top["raw"] == "0.618556701031" and top["rendered"] == "61.9%"
+    rows = calc["table"]["rows"]
+    share = calc["table"]["columns"].index("share")
+    rate = calc["table"]["columns"].index("pct")
+    flag = calc["table"]["columns"].index("flag")
+    assert [r[share] for r in rows] == ["61.9%", "20.6%", "17.5%", "0.0%"]
+    assert rows[0][rate] == "-14.6%" and rows[0][flag] == "—"      # 空格写「—」，不写 null
+    assert body["chain"][0]["render_ok"] is True
+
+
+# --------------------------------------------------------------------------
+# 导出页、可验证报告
+# --------------------------------------------------------------------------
+
+
+NODE_NS = {"__name__": "node-check"}
+try:
+    import shutil
+    import subprocess
+    from pathlib import Path
+
+    import app.bundle
+    import app.report_export
+    NODE = shutil.which("node")
+except Exception:  # pragma: no cover
+    NODE = None
+
+RUNTIME = Path(app.report_export.__file__).parent / "runtime.js" if NODE else None
+VERIFIER = Path(app.bundle.__file__).parent / "verifier.js" if NODE else None
+DOM = Path(__file__).parent.joinpath("test_analysis_pages.py").read_text(encoding="utf-8").split('DOM = r"""', 1)[1] \
+    .split('"""', 1)[0]
+
+
+def run_node(script: str, path: Path, payload: dict[str, Any]) -> Any:
+    done = subprocess.run([NODE, "-e", DOM + script, str(path)], input=json.dumps(payload), capture_output=True,
+                          text=True, timeout=120)
+    assert done.returncode == 0, done.stderr[-3000:]
+    return json.loads(done.stdout)
+
+
+RUN_RUNTIME = r"""
+document.getElementById('report-data').textContent = input.data
+new Function(fs.readFileSync(process.argv[1], 'utf8'))()
+const out = {}
+for (const seg of input.segs) {
+  const el = new El_('button')
+  for (const [k, v] of Object.entries(seg)) el.setAttribute(`data-${k}`, v)
+  el.textContent = seg.text
+  for (const f of document.getElementById('report').listeners.click || []) f({ target: el })
+  out[seg.seg] = document.getElementById('detail-body').textContent
+}
+process.stdout.write(JSON.stringify(out))
+"""
+
+RUN_VERIFIER = r"""
+document.getElementById('bundle-data').textContent = input.data
+document.getElementById('seal-hash').textContent = input.seal
+new Function(fs.readFileSync(process.argv[1], 'utf8'))()
+setTimeout(() => {
+  const reports = document.getElementById('reports')
+  const out = { opened: {} }
+  for (const btn of find(reports, (n) => n.attrs['data-num'])) {
+    for (const f of btn.listeners.click || []) f()
+    out.opened[btn.attrs['data-num']] = document.getElementById('detail-body').textContent
+  }
+  out.verified = document.documentElement.attrs['data-verified'] ?? null
+  process.stdout.write(JSON.stringify(out))
+}, 200)
+"""
+
+
+@pytest.mark.skipif(NODE is None, reason="没有 node，无法执行页面上的脚本")
+async def test_the_export_page_shows_percentages_in_the_calculation_step():
+    made = await sealed_run("[[a:A1]]。占比 [[a:A1.top_share]]，[[v:A1.r0.share]]。[[see:A1]]")
+    async with AsyncClient(transport=ASGITransport(app=asgi), base_url="http://test") as c:
+        html = (await c.get(f"/api/runs/{made['run']}/report.html")).text
+    import re
+
+    data = re.search(r'<script type="application/json" id="report-data">(.*?)</script>', html, re.S).group(1)
+    phrase = seg_id(made["doc"], "a:A1")
+    out = run_node(RUN_RUNTIME, RUNTIME, {"data": data, "segs": [
+        {"seg": phrase, "kind": "analysis", "state": "deterministic", "text": made["result"]["phrase"]}]})
+    body = out[phrase].replace(" ", "").replace(" ", "")
+    # 具名的数、结果表的格按结论记的格式写：top_share 61.9%、变化率 -14.6%，没有原始小数、没有 null
+    assert "top_share61.9%" in body and "top_k_share82.5%" in body
+    assert "占变化的比重" in body and "-14.6%" in body
+    assert "0.618556701" not in body and "null" not in body
+
+
+@pytest.mark.skipif(NODE is None, reason="没有 node，无法执行页面上的脚本")
+def test_the_verifiable_report_renders_the_conclusion_format():
+    bundle, _, _ = bundle_of("## 异动\n\n[[a:A1]]。华东占 [[a:A1.top_share]]，占比 [[v:A1.r0.share]]。[[see:A1]]")
+    out = run_node(RUN_VERIFIER, VERIFIER, {"data": json.dumps(bundle), "seal": bundle["meta"]["manifest_hash"]})
+    assert out["verified"] == ""
+    share = next(text for text in out["opened"].values() if "具名的数「top_share」" in text)
+    assert "正文显示61.9%（与按显示规则渲染的结果一致）" in share
+    cell = next(text for text in out["opened"].values() if "结果表 · 第 1 行 · share" in text)
+    assert "正文显示61.9%" in cell
\ No newline at end of file
diff --git a/backend/tests/test_analysis_evidence_api.py b/backend/tests/test_analysis_evidence_api.py
index 322c3801..7a46e3f8 100644
--- a/backend/tests/test_analysis_evidence_api.py
+++ b/backend/tests/test_analysis_evidence_api.py
@@ -170,7 +170,7 @@ async def test_named_values_and_result_cells_point_at_what_they_cite(client):
     assert value["segment"]["text"] == "61.9%" and value["chain"][0]["render_ok"] is True
     calc = value["chain"][2]
     assert next(v for v in calc["values"] if v["key"] == "top_share") == {
-        "key": "top_share", "raw": "0.618556701031", "rendered": "0.618556701", "cited": True}
+        "key": "top_share", "raw": "0.618556701031", "rendered": "61.9%", "cited": True}
     assert value["chain"][-2]["text"] == "复算一致：按封存的输入重新计算，结论逐项相同"
     assert value["note"] == "这个数取自分析结论的具名的数，由系统按固定规则显示，未经模型转写"
 
@@ -372,10 +372,10 @@ async def test_a_ratio_headline_closes_against_the_overall_ratio(client):
     made = await ratio_run()
     body = (await client.get(f"/api/runs/{made['run']}/evidence/segments/{seg_id(made['doc'], 'a:A1')}")).json()
     inputs = {s["role"]: s for s in body["chain"] if s["step"] == "analysis_input"}
-    assert inputs["headline_base"]["closure"] == {"mode": "ratio", "total_key": "r_base_total", "total": "0.035",
-                                                  "headline": "0.035", "closed": True}
-    assert inputs["headline_target"]["closure"] == {"mode": "ratio", "total_key": "r_target_total", "total": "0.053",
-                                                    "headline": "0.053", "closed": True}
+    assert inputs["headline_base"]["closure"] == {"mode": "ratio", "total_key": "r_base_total", "total": "3.5%",
+                                                  "headline": "3.5%", "closed": True}
+    assert inputs["headline_target"]["closure"] == {"mode": "ratio", "total_key": "r_target_total", "total": "5.3%",
+                                                    "headline": "5.3%", "closed": True}
     assert inputs["headline_base"]["role_label"] == "基期整体比率"
     assert inputs["headline_target"]["role_label"] == "本期整体比率"
     assert next(s for s in body["chain"] if s["step"] == "analysis_recompute")["status"] == "same"
diff --git a/backend/tests/test_analysis_pages.py b/backend/tests/test_analysis_pages.py
index f497873b..a7d19398 100644
--- a/backend/tests/test_analysis_pages.py
+++ b/backend/tests/test_analysis_pages.py
@@ -163,7 +163,8 @@ async def test_the_export_page_opens_the_analysis_steps_and_the_scope_numbers():
 
 async def test_the_export_page_speaks_of_the_overall_ratio_for_a_ratio_decomposition():
     """比率拆解的总数那一格是整体比率：导出页写「整体比率那一格」「整体比率 x 与各段分子之和 ÷ 分母之和 y 闭合」，不说
-    「各段之和」。可加拆解照旧（上一个用例的页面里没有「整体比率」）。"""
+    「各段之和」。比率按结论记的格式写成百分数（3.5%，不是 0.035），两格一致。可加拆解照旧（上一个用例的页面里没有
+    「整体比率」）。"""
     made = await ratio_run()
     async with AsyncClient(transport=ASGITransport(app=asgi), base_url="http://test") as client:
         html = (await client.get(f"/api/runs/{made['run']}/report.html")).text
@@ -172,9 +173,9 @@ async def test_the_export_page_speaks_of_the_overall_ratio_for_a_ratio_decomposi
     out = run_node(RUN_RUNTIME, RUNTIME, {"data": data, "segs": [
         {"seg": phrase, "kind": "analysis", "state": "deterministic", "text": made["result"]["phrase"]}]})
     body = out[phrase]["body"]
-    for text in ("基期整体比率", "整体比率那一格", "第 1 行「rate」 = 0.035",
-                 "整体比率 0.035 与各段分子之和 ÷ 分母之和 0.035 闭合",
-                 "整体比率 0.053 与各段分子之和 ÷ 分母之和 0.053 闭合"):
+    for text in ("基期整体比率", "整体比率那一格", "自身效应", "结构效应",
+                 "整体比率 3.5% 与各段分子之和 ÷ 分母之和 3.5% 闭合",
+                 "整体比率 5.3% 与各段分子之和 ÷ 分母之和 5.3% 闭合"):
         assert text in body, text
     assert "各段之和 0.0" not in body
 
diff --git a/backend/tests/test_analysis_run.py b/backend/tests/test_analysis_run.py
index 85b07b9a..c44d5423 100644
--- a/backend/tests/test_analysis_run.py
+++ b/backend/tests/test_analysis_run.py
@@ -44,8 +44,10 @@ async def _run(op, mode, params, *, probe=None, **kw):
 
 
 def _assert_shape(result):
-    """结论的字段、取值严格按契约。"""
-    assert set(result) == set(c.AnalysisResult.__annotations__)
+    """结论的字段、取值严格按契约。两样可选的（第 2 波 S1 加）：结论的 value_formats（具名的数的换算）、结果表的
+    formats（以比率存的列的换算）——都为空时整项不写，和以前的结论一字不差。"""
+    assert set(result) <= set(c.AnalysisResult.__annotations__)
+    assert set(c.AnalysisResult.__annotations__) - set(result) <= {"value_formats"}
     assert result["op"] in c.OPS and result["method"] == c.METHODS[(result["op"], result["mode"])]
     assert result["status"] in ("ok", "refused")
     if result["status"] == "refused":
@@ -57,11 +59,15 @@ def _assert_shape(result):
         assert all(isinstance(s, list) and all(isinstance(x, str) for x in s) for s in result["verdict"]["subjects"])
         assert result["verdict"]["direction"] in (None, "up", "down")
         table = result["table"]
-        assert set(table) == {"columns", "labels", "rows", "column_types"}
+        assert set(table) - {"formats"} == {"columns", "labels", "rows", "column_types"}
         assert set(table["labels"]) == set(table["columns"]) and set(table["column_types"]) <= set(table["columns"])
         assert list(table["column_types"]) == [c for c in table["columns"] if c in table["column_types"]]
         assert set(table["column_types"].values()) <= {"number", "text", "date", "datetime", "time", "boolean"}
         assert all(len(r) == len(table["columns"]) for r in table["rows"])
+        assert set(table.get("formats") or {}) <= set(table["columns"])
+        assert set((table.get("formats") or {}).values()) <= set(c.FORMAT_CONVS)
+    assert set(result.get("value_formats") or {}) <= set(result["values"])
+    assert set((result.get("value_formats") or {}).values()) <= set(c.FORMAT_CONVS)
     assert all(v is None or isinstance(v, str) for v in result["values"].values())
     assert all(set(cv) == {"code", "text"} and cv["code"] in c.CAVEATS for cv in result["caveats"])
     assert set(result["settings"]) <= set(c.DEFAULTS)
diff --git a/backend/tests/test_bundle_shown.py b/backend/tests/test_bundle_shown.py
index a29efb6f..617cec9b 100644
--- a/backend/tests/test_bundle_shown.py
+++ b/backend/tests/test_bundle_shown.py
@@ -413,7 +413,7 @@ def test_an_unknown_cell_display_version_cannot_pass():
 
     bundle = formats_bundle()
     art, doc = doc_of(bundle)
-    doc["cell_display"] = 4
+    doc["cell_display"] = 9
     got = verdicts(reseal(bundle, art, doc))
     cells = [n for n in got["numbers"] if n["ref"].startswith("v:")]
     assert cells and all(n["result"] == "na" and n["reason"].startswith("无法核对显示文字：") for n in cells)
diff --git a/docs/en/analysis.md b/docs/en/analysis.md
index f023a838..8e55d826 100644
--- a/docs/en/analysis.md
+++ b/docs/en/analysis.md
@@ -143,6 +143,7 @@ The built-in template 11, Anomaly Analysis (Traceable), is built this way: fetch
 ```
 
 - The Analysis Conclusions section of the writing catalog lists each conclusion's number, verdict phrase, named values, the top 10 segments of its result table, and caveats or the reason it couldn't be done.
+- The system shows shares and change rates in analysis conclusions as percentages (`[[a:A1.top_share]]` and `[[v:A1.r0.share]]` render as 62.0%), and changes in rates in percentage points (1.2 percentage points); writers cite them as usual and do not add a conversion. A conversion the writer does write is kept.
 - Conclusions from analysis cards can always be cited. In formal issuance at the governed level, analyses an agent ran on the spot with analysis tools follow the same rule as the cells it queried: they can be cited only when the issuance contract turns on Cell Citations (they can still serve as the basis at the end of a sentence).
 - The measure name in a verdict phrase comes from the agent or the template; the system first strips digits, percent signs, units, and citation markers from it and cuts it to 20 characters, so text written by the system never smuggles in numbers written by the model.
 
diff --git a/docs/en/evidence.md b/docs/en/evidence.md
index 3e437a2b..573892c3 100644
--- a/docs/en/evidence.md
+++ b/docs/en/evidence.md
@@ -98,7 +98,7 @@ In the report-writer node (`report`), the model writes only citation markers, an
 [[see:m:gmv,Q1,A1]]                        basis of a claim sentence; attached at the end of the sentence, not rendered
 ```
 
-Analysis conclusions are numbered A1, A2, … in ledger order, and the writing catalog has a separate Analysis Conclusions section. Judgment words such as "mainly from", "unusual", or "rising steadily" must cite a matching analysis conclusion and agree with its verdict; see [Analysis claim check](analysis.md#analysis-claim-check).
+Analysis conclusions are numbered A1, A2, … in ledger order, and the writing catalog has a separate Analysis Conclusions section. The system shows shares and change rates in analysis conclusions as percentages (`[[a:A1.top_share]]` renders as 62.0%), and changes in rates in percentage points (1.2 percentage points); writers cite them as usual and do not add a conversion. Judgment words such as "mainly from", "unusual", or "rising steadily" must cite a matching analysis conclusion and agree with its verdict; see [Analysis claim check](analysis.md#analysis-claim-check).
 
 ### Handling violations
 
diff --git a/docs/zh-CN/analysis.md b/docs/zh-CN/analysis.md
index d0f442b6..9c8a0224 100644
--- a/docs/zh-CN/analysis.md
+++ b/docs/zh-CN/analysis.md
@@ -143,6 +143,7 @@
 ```
 
 - 写作目录里「分析结论」一段列出每份结论的编号、判定语、具名的数、结果表的前 10 段、附带说明或算不了的原因。
+- 分析结论里的占比、变化率由系统按百分数显示（`[[a:A1.top_share]]`、`[[v:A1.r0.share]]` 写出来是 62.0%），比率的变化按百分点（1.2 个百分点），写作者照常引用、不用自己写换算；自己写了换算的照他的。
 - 分析卡的结论总能引用。受管级别的正式出具里，Agent 用分析工具现场做的分析和它查的格同一个规矩：出具契约开了「单元格引用」才能引用（句末依据照样能挂）。
 - 判定语里的度量叫法是 Agent 或模板给的，系统先去掉里面的数字、百分号、单位和引用标记，截到 20 字以内：系统写的字里不会夹带模型写的数。
 
diff --git a/docs/zh-CN/evidence.md b/docs/zh-CN/evidence.md
index 731e6b69..e4486049 100644
--- a/docs/zh-CN/evidence.md
+++ b/docs/zh-CN/evidence.md
@@ -98,7 +98,7 @@ flowchart LR
 [[see:m:gmv,Q1,A1]]                        结论句的依据，挂在句末，不渲染
 ```
 
-分析结论按台账先后编成 A1、A2……，写作目录里另有一段「分析结论」。「主要来自」「异常」「持续上升」这类判断词要挂对应的分析结论、判定对得上，见[分析说法核对](analysis.md#分析说法核对)。
+分析结论按台账先后编成 A1、A2……，写作目录里另有一段「分析结论」。分析结论里的占比、变化率由系统按百分数显示（`[[a:A1.top_share]]` 写出来是 62.0%），比率的变化按百分点（1.2 个百分点），写作者照常引用、不用自己写换算。「主要来自」「异常」「持续上升」这类判断词要挂对应的分析结论、判定对得上，见[分析说法核对](analysis.md#分析说法核对)。
 
 ### 违规怎么处理
 
diff --git a/frontend/src/run/EvidenceAnalysis.tsx b/frontend/src/run/EvidenceAnalysis.tsx
index b3f8a599..e831e7ee 100644
--- a/frontend/src/run/EvidenceAnalysis.tsx
+++ b/frontend/src/run/EvidenceAnalysis.tsx
@@ -192,7 +192,8 @@ function MethodPart({ step }: { step: EvidenceAnalysisStep }) {
   )
 }
 
-/** 计算：关键数的代入式、具名的数（被引用的那个描出来）、结果表（被引用的那一格高亮，遮罩的列写「已遮罩」） */
+/** 计算：关键数的代入式、具名的数（被引用的那个描出来）、结果表（被引用的那一格高亮，遮罩的列写「已遮罩」）。
+ *  数与格都是服务端按结论记的格式写好的字（占比、变化率是百分数，空格是「—」），这里照原样显示 */
 function CalcPart({ step }: { step: EvidenceAnalysisStep }) {
   const table = step.table && Array.isArray(step.table.columns) && Array.isArray(step.table.rows) ? step.table : null
   const labels = table?.labels ?? {}
diff --git a/frontend/src/run/__tests__/evidence-analysis.json b/frontend/src/run/__tests__/evidence-analysis.json
index a4780114..746cb12b 100644
--- a/frontend/src/run/__tests__/evidence-analysis.json
+++ b/frontend/src/run/__tests__/evidence-analysis.json
@@ -1,9 +1,9 @@
 {
  "_comment": "分析能力一轮（W2-c）证据面板、正文的前端夹具：/tmp/agentlab-fix/w2c/test_zz_w2c_capture.py 生成（照 backend/tests/test_analysis_e2e_claims.py 的虚构零售库 shop，数据目录标了地区、渠道是属性；Agent、写作模型都换成脚本）。agent：Agent 查两次库、写错一次变化拆解、做变化拆解（A1 主要来自华东）、波动基线（A2 两周历史不够，算不了）、可比性检查（A3），报告引用 A1 的判定语、结果表的一格、具名的数，另有三句说法违规（主要来自华南、异常、由于），文档末尾系统写的分析范围；card：分析卡两项（A1 波动基线算不了、A2 按地区拆解），报告里有一张表引用结果表的格；metric：口径卡的环比用到两份同形查询，指标上挂着可比性。片段接口、证据图、审计、分析范围里每个编号的出处链（GET …/evidence/analyses/{alias}）都是真后端答的。只用通用名",
  "agent": {
-  "run_id": "ed3de345aac143089fb3d0777d9056ec",
+  "run_id": "6d4d8c841a7d4900a3495f24bc932afa",
   "run_class": "exploratory",
-  "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389",
+  "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc",
   "doc": {
    "analysis_claims": {
     "lexicon": 1,
@@ -109,7 +109,7 @@
        "see": [
         {
          "alias": "A1",
-         "eid": "ev:analysis:10fb652eb3e403a8",
+         "eid": "ev:analysis:a7edbc13c92c31a0",
          "kind": "analysis",
          "locator": {},
          "ref": "A1",
@@ -131,7 +131,7 @@
         {
          "cite": {
           "alias": "A1",
-          "eid": "ev:analysis:10fb652eb3e403a8",
+          "eid": "ev:analysis:a7edbc13c92c31a0",
           "kind": "analysis",
           "locator": {},
           "ref": "A1",
@@ -202,7 +202,7 @@
         {
          "cite": {
           "alias": "A1",
-          "eid": "ev:cell:164abd36aeac4c3b",
+          "eid": "ev:cell:a7deaec3f4ed6ab6",
           "kind": "cell",
           "locator": {
            "column": "delta",
@@ -238,7 +238,7 @@
          "cite": {
           "alias": "A1",
           "conv": "pct",
-          "eid": "ev:analysis:efc6267321553bc3",
+          "eid": "ev:analysis:5517f6de3df56204",
           "kind": "analysis",
           "locator": {
            "key": "top_share"
@@ -290,7 +290,7 @@
        "see": [
         {
          "alias": "A1",
-         "eid": "ev:analysis:10fb652eb3e403a8",
+         "eid": "ev:analysis:a7edbc13c92c31a0",
          "kind": "analysis",
          "locator": {},
          "ref": "A1",
@@ -402,18 +402,753 @@
        ]
       }
      ]
+    },
+    {
+     "id": "b6",
+     "type": "paragraph",
+     "units": [
+      {
+       "cites": [
+        "A1"
+       ],
+       "id": "u6",
+       "kind": "claim",
+       "see": [],
+       "segments": [
+        {
+         "id": "s17",
+         "kind": "structural",
+         "span": [
+          115,
+          117
+         ],
+         "state": "neutral",
+         "text": "\n\n"
+        },
+        {
+         "id": "s18",
+         "kind": "text",
+         "span": [
+          117,
+          121
+         ],
+         "state": "neutral",
+         "text": "变化率 "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "conv": "pct",
+          "eid": "ev:cell:32d18640349e7717",
+          "kind": "cell",
+          "locator": {
+           "column": "pct",
+           "row": 0
+          },
+          "ref": "A1.r0.pct",
+          "rendered": "-30.0%",
+          "role": "value",
+          "status": "resolved",
+          "value": -0.3
+         },
+         "id": "s19",
+         "kind": "number",
+         "ref": "v:A1.r0.pct",
+         "span": [
+          121,
+          127
+         ],
+         "state": "deterministic",
+         "text": "-30.0%"
+        },
+        {
+         "id": "s20",
+         "kind": "text",
+         "span": [
+          127,
+          131
+         ],
+         "state": "neutral",
+         "text": "、比重 "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "conv": "pct",
+          "eid": "ev:cell:b36848a6055a5628",
+          "kind": "cell",
+          "locator": {
+           "column": "share",
+           "row": 0
+          },
+          "ref": "A1.r0.share",
+          "rendered": "88.2%",
+          "role": "value",
+          "status": "resolved",
+          "value": 0.882352941176
+         },
+         "id": "s21",
+         "kind": "number",
+         "ref": "v:A1.r0.share",
+         "span": [
+          131,
+          136
+         ],
+         "state": "deterministic",
+         "text": "88.2%"
+        },
+        {
+         "id": "s22",
+         "kind": "text",
+         "span": [
+          136,
+          140
+         ],
+         "state": "neutral",
+         "text": "、占比 "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "conv": "pct",
+          "eid": "ev:analysis:5517f6de3df56204",
+          "kind": "analysis",
+          "locator": {
+           "key": "top_share"
+          },
+          "ref": "A1.top_share",
+          "rendered": "88.2%",
+          "role": "value",
+          "status": "resolved",
+          "value": 0.882352941176
+         },
+         "id": "s23",
+         "kind": "number",
+         "ref": "a:A1.top_share",
+         "span": [
+          140,
+          145
+         ],
+         "state": "deterministic",
+         "text": "88.2%"
+        },
+        {
+         "id": "s24",
+         "kind": "text",
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+         "state": "neutral",
+         "text": "。"
+        }
+       ],
+       "span": [
+        117,
+        146
+       ]
+      }
+     ]
+    },
+    {
+     "id": "b7",
+     "type": "table",
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+       "id": "u7",
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+         "state": "neutral",
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+      },
+      {
+       "cites": [],
+       "id": "u8",
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+         "state": "neutral",
+         "text": " | "
+        },
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+         "id": "s28",
+         "kind": "text",
+         "span": [
+          155,
+          157
+         ],
+         "state": "neutral",
+         "text": "变化"
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+       ],
+       "span": [
+        155,
+        157
+       ]
+      },
+      {
+       "cites": [],
+       "id": "u9",
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+         "state": "neutral",
+         "text": " | "
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+         "id": "s30",
+         "kind": "text",
+         "span": [
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+         ],
+         "state": "neutral",
+         "text": "变化率"
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+       ],
+       "span": [
+        160,
+        163
+       ]
+      },
+      {
+       "cites": [],
+       "id": "u10",
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+         "state": "neutral",
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+        {
+         "id": "s32",
+         "kind": "text",
+         "span": [
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+         ],
+         "state": "neutral",
+         "text": "占变化的比重"
+        }
+       ],
+       "span": [
+        166,
+        172
+       ]
+      },
+      {
+       "cites": [
+        "A1"
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+       "kind": "claim",
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+         "state": "neutral",
+         "text": " |\n| --- | --- | --- | --- |\n| "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "eid": "ev:cell:cf44ef048f0ff6cf",
+          "kind": "cell",
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+          "ref": "A1.r0.region",
+          "rendered": "华东",
+          "role": "value",
+          "status": "resolved",
+          "value": "华东"
+         },
+         "id": "s34",
+         "kind": "value",
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+         "text": "华东"
+        }
+       ],
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+      },
+      {
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+        "A1"
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+         "state": "neutral",
+         "text": " | "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "conv": "pct",
+          "eid": "ev:cell:32d18640349e7717",
+          "kind": "cell",
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+         "state": "deterministic",
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+        }
+       ],
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+      },
+      {
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+        "A1"
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+         "state": "neutral",
+         "text": " | "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "conv": "pct",
+          "eid": "ev:cell:b36848a6055a5628",
+          "kind": "cell",
+          "locator": {
+           "column": "share",
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+          },
+          "ref": "A1.r0.share",
+          "rendered": "88.2%",
+          "role": "value",
+          "status": "resolved",
+          "value": 0.882352941176
+         },
+         "id": "s40",
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+         "ref": "v:A1.r0.share",
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+         "state": "deterministic",
+         "text": "88.2%"
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+       ],
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+       ]
+      },
+      {
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+        "A1"
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+        {
+         "id": "s41",
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+         "span": [
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+         "state": "neutral",
+         "text": " |\n| "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "eid": "ev:cell:15977323c7fb25b1",
+          "kind": "cell",
+          "locator": {
+           "column": "region",
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+          },
+          "ref": "A1.r1.region",
+          "rendered": "华南",
+          "role": "value",
+          "status": "resolved",
+          "value": "华南"
+         },
+         "id": "s42",
+         "kind": "value",
+         "ref": "v:A1.r1.region",
+         "span": [
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+         "state": "deterministic",
+         "text": "华南"
+        }
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+        "A1"
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+         "state": "neutral",
+         "text": " | "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "conv": "pct",
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+         "ref": "v:A1.r1.pct",
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+         "state": "deterministic",
+         "text": "-2.5%"
+        }
+       ],
+       "span": [
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+       ]
+      },
+      {
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+        "A1"
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+         "state": "neutral",
+         "text": " | "
+        },
+        {
+         "cite": {
+          "alias": "A1",
+          "conv": "pct",
+          "eid": "ev:cell:379dcf4b1b6947b1",
+          "kind": "cell",
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+          "ref": "A1.r1.share",
+          "rendered": "5.9%",
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+         "state": "deterministic",
+         "text": "5.9%"
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+         "state": "neutral",
+         "text": " |"
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+      }
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-     "eid": "ev:analysis:10fb652eb3e403a8",
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-      "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901"
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      "label": "变化拆解（可加） · 销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
@@ -436,12 +1171,12 @@
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-     "artifact": "2e4139852d4ca46f8ee328ce44cb2ddde14f79e54c1371b3a77e15bf232b0122",
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      "call_id": "call_4",
-     "eid": "ev:analysis:436f6b967aa1414c",
+     "eid": "ev:analysis:d89ecaae59a168f0",
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-      "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc"
+      "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3"
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      "kind": "analysis",
      "label": "波动基线 · 波动基线没有做成：只有 0 期历史，至少要 8 期",
@@ -454,12 +1189,12 @@
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-     "artifact": "afa2ea2ba75e5eb20e7f3372e3a9dea164eddb0338fa51c6b2fd9a808c09072a",
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      "label": "可比性检查 · 两期可比，窗口长度、整期、星期构成核对不了",
@@ -480,13 +1215,13 @@
     },
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-     "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
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       "week",
       "gmv"
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-     "eid": "ev:query:ad723c3d0c0ae28c",
+     "eid": "ev:query:9c920fc7352dde33",
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      "kind": "query",
      "label": "db_query__shop · 2 行",
@@ -496,18 +1231,18 @@
      "source": "shop",
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-     "via": "6c603c020e74d0ad9022eb472d9a8cb4e1c3fc95b60f276a53e0ebfce9ba16d8"
+     "via": "47b58ee48f462b179b07112a04e036570be292673abc8ed9886d93141d0ef2d3"
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+     "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
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       "region",
       "week",
       "gmv"
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-     "eid": "ev:query:c390b8b187033425",
+     "eid": "ev:query:42ab65ce25770550",
      "exec": 1,
      "kind": "query",
      "label": "db_query__shop · 8 行",
@@ -517,34 +1252,35 @@
      "source": "shop",
      "tool": "db_query__shop",
      "truncated": false,
-     "via": "6f9f2696d27966294fc257843c079ff745509f15bdfe64ef6ee23ca24318ddc5"
+     "via": "59b5ceca23fcd9ff344ce565b29b9d877af673dafbe98782b7b37aeaf1a9a665"
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+   "cell_display": 4,
    "entity_syntax": 2,
-   "markdown": "## 本周销售\n\n销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）。\n\n华东的变化为 -300，占总变化的 88.2%。\n\n其中下降主要来自华南。\n\n销售额出现异常下降。\n\n由于促销结束，订单有所减少。",
+   "markdown": "## 本周销售\n\n销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）。\n\n华东的变化为 -300，占总变化的 88.2%。\n\n其中下降主要来自华南。\n\n销售额出现异常下降。\n\n由于促销结束，订单有所减少。\n\n变化率 -30.0%、比重 88.2%、占比 88.2%。\n\n| 地区 | 变化 | 变化率 | 占变化的比重 |\n| --- | --- | --- | --- |\n| 华东 | -300 | -30.0% | 88.2% |\n| 华南 | -20 | -2.5% | 5.9% |",
    "node_id": "write",
    "provenance": 1,
-   "run_id": "ed3de345aac143089fb3d0777d9056ec",
+   "run_id": "6d4d8c841a7d4900a3495f24bc932afa",
    "schema": "agentlab.report/1",
-   "source": "## 本周销售\n\n[[a:A1]]。[[see:A1]]\n\n华东的变化为 [[v:A1.r0.delta]]，占总变化的 [[a:A1.top_share|pct]]。\n\n其中下降主要来自华南。[[see:A1]]\n\n销售额出现异常下降。\n\n由于促销结束，订单有所减少。",
+   "source": "## 本周销售\n\n[[a:A1]]。[[see:A1]]\n\n华东的变化为 [[v:A1.r0.delta]]，占总变化的 [[a:A1.top_share|pct]]。\n\n其中下降主要来自华南。[[see:A1]]\n\n销售额出现异常下降。\n\n由于促销结束，订单有所减少。\n\n变化率 [[v:A1.r0.pct]]、比重 [[v:A1.r0.share]]、占比 [[a:A1.top_share]]。\n\n[[table:A1 cols=region,delta,pct,share rows=0-1]]",
    "stats": {
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@@ -603,7 +1339,7 @@
    "s3": {
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+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
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@@ -618,7 +1354,7 @@
      "ref": "a:A1",
      "cite": {
       "alias": "A1",
-      "eid": "ev:analysis:10fb652eb3e403a8",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
       "kind": "analysis",
       "locator": {},
       "ref": "A1",
@@ -659,8 +1395,8 @@
       "call_id": "call_3",
       "node_id": "bot",
       "label": "问数据",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
-      "eid": "ev:analysis:10fb652eb3e403a8",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
       "eid_ok": true,
       "cited": {
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@@ -778,7 +1514,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
        },
        {
         "key": "segments",
@@ -798,7 +1534,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        },
        {
         "key": "top_key",
@@ -808,7 +1544,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        }
       ],
       "formulas": [
@@ -854,39 +1590,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
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          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
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          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
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          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
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@@ -901,7 +1637,7 @@
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       "role_label": "基期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
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       "column": "gmv",
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@@ -923,7 +1659,7 @@
       "ref": "Q1.r1.gmv",
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       "role_label": "本期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -945,7 +1681,7 @@
       "ref": "Q2",
       "role": "data",
       "role_label": "数据",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "columns": [
        "region",
        "week",
@@ -959,7 +1695,7 @@
      {
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       "alias": "Q1",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -1011,7 +1747,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
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         "table": "orders",
@@ -1023,7 +1759,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -1211,7 +1947,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
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         "table": "orders",
@@ -1231,7 +1967,7 @@
       "step": "analysis_seal",
       "alias": "A1",
       "node_id": "bot",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -1252,7 +1988,7 @@
    "s7": {
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      "node_id": "write",
-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
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@@ -1267,7 +2003,7 @@
      "ref": "v:A1.r0.delta",
      "cite": {
       "alias": "A1",
-      "eid": "ev:cell:164abd36aeac4c3b",
+      "eid": "ev:cell:a7deaec3f4ed6ab6",
       "kind": "cell",
       "locator": {
        "column": "delta",
@@ -1312,8 +2048,8 @@
       "call_id": "call_3",
       "node_id": "bot",
       "label": "问数据",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
-      "eid": "ev:analysis:10fb652eb3e403a8",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
       "eid_ok": true,
       "cited": {
        "what": "cell",
@@ -1435,7 +2171,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
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        {
         "key": "segments",
@@ -1455,7 +2191,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
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        {
         "key": "top_key",
@@ -1465,7 +2201,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
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       ],
       "formulas": [
@@ -1511,39 +2247,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
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          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
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          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
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          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
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@@ -1563,7 +2299,7 @@
       "ref": "Q1.r0.gmv",
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       "role_label": "基期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
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       "column": "gmv",
       "value": "2700",
@@ -1585,7 +2321,7 @@
       "ref": "Q1.r1.gmv",
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       "role_label": "本期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -1607,7 +2343,7 @@
       "ref": "Q2",
       "role": "data",
       "role_label": "数据",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "columns": [
        "region",
        "week",
@@ -1621,7 +2357,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -1673,7 +2409,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
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         "table": "orders",
@@ -1685,7 +2421,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -1873,7 +2609,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
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         "table": "orders",
@@ -1893,7 +2629,7 @@
       "step": "analysis_seal",
       "alias": "A1",
       "node_id": "bot",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -1914,7 +2650,7 @@
    "s9": {
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      "node_id": "write",
-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
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@@ -1930,7 +2666,7 @@
      "cite": {
       "alias": "A1",
       "conv": "pct",
-      "eid": "ev:analysis:efc6267321553bc3",
+      "eid": "ev:analysis:5517f6de3df56204",
       "kind": "analysis",
       "locator": {
        "key": "top_share"
@@ -1974,8 +2710,8 @@
       "call_id": "call_3",
       "node_id": "bot",
       "label": "问数据",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
-      "eid": "ev:analysis:10fb652eb3e403a8",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
       "eid_ok": true,
       "cited": {
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@@ -2096,7 +2832,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
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         "key": "segments",
@@ -2116,7 +2852,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
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         "key": "top_key",
@@ -2126,7 +2862,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412",
+        "rendered": "88.2%",
         "cited": true
        }
       ],
@@ -2173,39 +2909,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
         ],
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          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
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          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
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          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
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@@ -2220,7 +2956,7 @@
       "ref": "Q1.r0.gmv",
       "role": "headline_base",
       "role_label": "基期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
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       "value": "2700",
@@ -2242,7 +2978,7 @@
       "ref": "Q1.r1.gmv",
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       "role_label": "本期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -2264,7 +3000,7 @@
       "ref": "Q2",
       "role": "data",
       "role_label": "数据",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "columns": [
        "region",
        "week",
@@ -2278,7 +3014,7 @@
      {
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       "alias": "Q1",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -2330,7 +3066,7 @@
        "gmv": "number",
        "week": "text"
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-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
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         "table": "orders",
@@ -2342,7 +3078,7 @@
      {
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       "alias": "Q2",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -2530,7 +3266,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
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         "table": "orders",
@@ -2550,7 +3286,7 @@
       "step": "analysis_seal",
       "alias": "A1",
       "node_id": "bot",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -2571,7 +3307,7 @@
    "s12": {
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-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
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@@ -2639,7 +3375,7 @@
    "s14": {
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-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
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@@ -2703,7 +3439,7 @@
    "s16": {
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-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
     },
     "segment": {
      "id": "s16",
@@ -2762,21 +3498,7261 @@
     "redacted": {
      "columns": []
     }
+   },
+   "s19": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s19",
+     "text": "-30.0%",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      121,
+      127
+     ],
+     "unit": "u6",
+     "ref": "v:A1.r0.pct",
+     "cite": {
+      "alias": "A1",
+      "conv": "pct",
+      "eid": "ev:cell:32d18640349e7717",
+      "kind": "cell",
+      "locator": {
+       "column": "pct",
+       "row": 0
+      },
+      "ref": "A1.r0.pct",
+      "rendered": "-30.0%",
+      "role": "value",
+      "status": "resolved",
+      "value": -0.3
+     }
+    },
+    "unit": {
+     "id": "u6",
+     "kind": "claim",
+     "text": "变化率 -30.0%、比重 88.2%、占比 88.2%。",
+     "span": [
+      117,
+      146
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": true,
+      "reason": null,
+      "message": null
+     }
+    },
+    "block": {
+     "id": "b6",
+     "type": "paragraph"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 0,
+       "column": "pct",
+       "raw": "-0.3"
+      },
+      "rendered": "-30.0%",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          0,
+          "pct"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
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+       1
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+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
+       "rows": [
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+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s21": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s21",
+     "text": "88.2%",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      131,
+      136
+     ],
+     "unit": "u6",
+     "ref": "v:A1.r0.share",
+     "cite": {
+      "alias": "A1",
+      "conv": "pct",
+      "eid": "ev:cell:b36848a6055a5628",
+      "kind": "cell",
+      "locator": {
+       "column": "share",
+       "row": 0
+      },
+      "ref": "A1.r0.share",
+      "rendered": "88.2%",
+      "role": "value",
+      "status": "resolved",
+      "value": 0.882352941176
+     }
+    },
+    "unit": {
+     "id": "u6",
+     "kind": "claim",
+     "text": "变化率 -30.0%、比重 88.2%、占比 88.2%。",
+     "span": [
+      117,
+      146
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": true,
+      "reason": null,
+      "message": null
+     }
+    },
+    "block": {
+     "id": "b6",
+     "type": "paragraph"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 0,
+       "column": "share",
+       "raw": "0.882352941176"
+      },
+      "rendered": "88.2%",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          0,
+          "share"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s23": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s23",
+     "text": "88.2%",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      140,
+      145
+     ],
+     "unit": "u6",
+     "ref": "a:A1.top_share",
+     "cite": {
+      "alias": "A1",
+      "conv": "pct",
+      "eid": "ev:analysis:5517f6de3df56204",
+      "kind": "analysis",
+      "locator": {
+       "key": "top_share"
+      },
+      "ref": "A1.top_share",
+      "rendered": "88.2%",
+      "role": "value",
+      "status": "resolved",
+      "value": 0.882352941176
+     }
+    },
+    "unit": {
+     "id": "u6",
+     "kind": "claim",
+     "text": "变化率 -30.0%、比重 88.2%、占比 88.2%。",
+     "span": [
+      117,
+      146
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": true,
+      "reason": null,
+      "message": null
+     }
+    },
+    "block": {
+     "id": "b6",
+     "type": "paragraph"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "value",
+       "key": "top_share",
+       "raw": "0.882352941176",
+       "conv": "pct"
+      },
+      "rendered": "88.2%",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%",
+        "cited": true
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": []
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "这个数取自分析结论的具名的数，由系统按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s34": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s34",
+     "text": "华东",
+     "kind": "value",
+     "state": "deterministic",
+     "span": [
+      203,
+      205
+     ],
+     "unit": "u11",
+     "ref": "v:A1.r0.region",
+     "cite": {
+      "alias": "A1",
+      "eid": "ev:cell:cf44ef048f0ff6cf",
+      "kind": "cell",
+      "locator": {
+       "column": "region",
+       "row": 0
+      },
+      "ref": "A1.r0.region",
+      "rendered": "华东",
+      "role": "value",
+      "status": "resolved",
+      "value": "华东"
+     }
+    },
+    "unit": {
+     "id": "u11",
+     "kind": "claim",
+     "text": "华东",
+     "span": [
+      203,
+      205
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 0,
+       "column": "region",
+       "raw": "华东"
+      },
+      "rendered": "华东",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          0,
+          "region"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
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+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
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+        1
+       ],
+       "cols": [
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+       ],
+       "cells": [
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+         "gmv"
+        ],
+        [
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+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
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+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
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+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
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+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s36": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s36",
+     "text": "-300",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      208,
+      212
+     ],
+     "unit": "u12",
+     "ref": "v:A1.r0.delta",
+     "cite": {
+      "alias": "A1",
+      "eid": "ev:cell:a7deaec3f4ed6ab6",
+      "kind": "cell",
+      "locator": {
+       "column": "delta",
+       "row": 0
+      },
+      "ref": "A1.r0.delta",
+      "rendered": "-300",
+      "role": "value",
+      "status": "resolved",
+      "value": -300
+     }
+    },
+    "unit": {
+     "id": "u12",
+     "kind": "claim",
+     "text": "-300",
+     "span": [
+      208,
+      212
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 0,
+       "column": "delta",
+       "raw": "-300"
+      },
+      "rendered": "-300",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
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+          "delta"
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+       }
+      }
+     },
+     {
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+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
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+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
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+      "row_index": [
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+      "truncated": false,
+      "highlight": {
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+       "cols": [
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+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
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+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
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+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
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+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
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+       1,
+       2,
+       3,
+       4,
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+       6,
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+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
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+        2,
+        3,
+        4,
+        5,
+        6,
+        7
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+       "cols": [
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+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s38": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s38",
+     "text": "-30.0%",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      215,
+      221
+     ],
+     "unit": "u13",
+     "ref": "v:A1.r0.pct",
+     "cite": {
+      "alias": "A1",
+      "conv": "pct",
+      "eid": "ev:cell:32d18640349e7717",
+      "kind": "cell",
+      "locator": {
+       "column": "pct",
+       "row": 0
+      },
+      "ref": "A1.r0.pct",
+      "rendered": "-30.0%",
+      "role": "value",
+      "status": "resolved",
+      "value": -0.3
+     }
+    },
+    "unit": {
+     "id": "u13",
+     "kind": "claim",
+     "text": "-30.0%",
+     "span": [
+      215,
+      221
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 0,
+       "column": "pct",
+       "raw": "-0.3"
+      },
+      "rendered": "-30.0%",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          0,
+          "pct"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s40": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s40",
+     "text": "88.2%",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      224,
+      229
+     ],
+     "unit": "u14",
+     "ref": "v:A1.r0.share",
+     "cite": {
+      "alias": "A1",
+      "conv": "pct",
+      "eid": "ev:cell:b36848a6055a5628",
+      "kind": "cell",
+      "locator": {
+       "column": "share",
+       "row": 0
+      },
+      "ref": "A1.r0.share",
+      "rendered": "88.2%",
+      "role": "value",
+      "status": "resolved",
+      "value": 0.882352941176
+     }
+    },
+    "unit": {
+     "id": "u14",
+     "kind": "claim",
+     "text": "88.2%",
+     "span": [
+      224,
+      229
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 0,
+       "column": "share",
+       "raw": "0.882352941176"
+      },
+      "rendered": "88.2%",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          0,
+          "share"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s42": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s42",
+     "text": "华南",
+     "kind": "value",
+     "state": "deterministic",
+     "span": [
+      234,
+      236
+     ],
+     "unit": "u15",
+     "ref": "v:A1.r1.region",
+     "cite": {
+      "alias": "A1",
+      "eid": "ev:cell:15977323c7fb25b1",
+      "kind": "cell",
+      "locator": {
+       "column": "region",
+       "row": 1
+      },
+      "ref": "A1.r1.region",
+      "rendered": "华南",
+      "role": "value",
+      "status": "resolved",
+      "value": "华南"
+     }
+    },
+    "unit": {
+     "id": "u15",
+     "kind": "claim",
+     "text": "华南",
+     "span": [
+      234,
+      236
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 1,
+       "column": "region",
+       "raw": "华南"
+      },
+      "rendered": "华南",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          1,
+          "region"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
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+       "cols": [
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+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
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+        [
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+         "region"
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+        [
+         0,
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+        ],
+        [
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+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
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+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s44": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s44",
+     "text": "-20",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      239,
+      242
+     ],
+     "unit": "u16",
+     "ref": "v:A1.r1.delta",
+     "cite": {
+      "alias": "A1",
+      "eid": "ev:cell:50fa5438a057327a",
+      "kind": "cell",
+      "locator": {
+       "column": "delta",
+       "row": 1
+      },
+      "ref": "A1.r1.delta",
+      "rendered": "-20",
+      "role": "value",
+      "status": "resolved",
+      "value": -20
+     }
+    },
+    "unit": {
+     "id": "u16",
+     "kind": "claim",
+     "text": "-20",
+     "span": [
+      239,
+      242
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 1,
+       "column": "delta",
+       "raw": "-20"
+      },
+      "rendered": "-20",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
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+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
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+     },
+     {
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+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
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+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
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+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
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+      "column": "gmv",
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+      "node_id": "bot",
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+      "closure": {
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+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
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+       [
+        "2026-W42",
+        2360
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+      "masked": [],
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+      "sealed": true,
+      "column_types": {
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+      "catalog": [
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+        "version": 1
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+      ],
+      "input_of": "A1"
+     },
+     {
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+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
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+        "2026-W41",
+        1000
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+       [
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+        "2026-W42",
+        700
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+       [
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+        "2026-W41",
+        600
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+       [
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+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
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+       2,
+       3,
+       4,
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+       7
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+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
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+        2,
+        3,
+        4,
+        5,
+        6,
+        7
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+       "cols": [
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+       ],
+       "cells": [
+        [
+         0,
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+        ],
+        [
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+         "region"
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+        [
+         2,
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+        ],
+        [
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+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
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+        [
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+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
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+        [
+         2,
+         "week"
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+        [
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+        ],
+        [
+         4,
+         "week"
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+        [
+         5,
+         "week"
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+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
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+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
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+    }
+   },
+   "s46": {
+    "report": {
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+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
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+     "text": "-2.5%",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
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+     ],
+     "unit": "u17",
+     "ref": "v:A1.r1.pct",
+     "cite": {
+      "alias": "A1",
+      "conv": "pct",
+      "eid": "ev:cell:1bb9cb78a05f83a2",
+      "kind": "cell",
+      "locator": {
+       "column": "pct",
+       "row": 1
+      },
+      "ref": "A1.r1.pct",
+      "rendered": "-2.5%",
+      "role": "value",
+      "status": "resolved",
+      "value": -0.025
+     }
+    },
+    "unit": {
+     "id": "u17",
+     "kind": "claim",
+     "text": "-2.5%",
+     "span": [
+      245,
+      250
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 1,
+       "column": "pct",
+       "raw": "-0.025"
+      },
+      "rendered": "-2.5%",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          1,
+          "pct"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
+   },
+   "s48": {
+    "report": {
+     "node_id": "write",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
+    },
+    "segment": {
+     "id": "s48",
+     "text": "5.9%",
+     "kind": "number",
+     "state": "deterministic",
+     "span": [
+      253,
+      257
+     ],
+     "unit": "u18",
+     "ref": "v:A1.r1.share",
+     "cite": {
+      "alias": "A1",
+      "conv": "pct",
+      "eid": "ev:cell:379dcf4b1b6947b1",
+      "kind": "cell",
+      "locator": {
+       "column": "share",
+       "row": 1
+      },
+      "ref": "A1.r1.share",
+      "rendered": "5.9%",
+      "role": "value",
+      "status": "resolved",
+      "value": 0.0588235294118
+     }
+    },
+    "unit": {
+     "id": "u18",
+     "kind": "claim",
+     "text": "5.9%",
+     "span": [
+      253,
+      257
+     ],
+     "cites": [
+      "A1"
+     ],
+     "on_demand": {
+      "available": false,
+      "reason": "not_a_claim",
+      "message": "这句不是结论句（标题、连接性文字、表格单元格或代码），无需模型裁判"
+     }
+    },
+    "block": {
+     "id": "b7",
+     "type": "table"
+    },
+    "chain": [
+     {
+      "step": "analysis",
+      "alias": "A1",
+      "op": "decompose",
+      "mode": "additive",
+      "method_label": "变化拆解（可加）",
+      "origin": "agent",
+      "call_id": "call_3",
+      "node_id": "bot",
+      "label": "问数据",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
+      "eid_ok": true,
+      "cited": {
+       "what": "cell",
+       "row": 1,
+       "column": "share",
+       "raw": "0.0588235294118"
+      },
+      "rendered": "5.9%",
+      "render_ok": true,
+      "hash_ok": true,
+      "status": "ok",
+      "reason_code": null,
+      "reason": null,
+      "phrase": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
+      "criterion": "一段占总变化的一半以上算主要来源，最多前 2 段合计占 80% 以上算集中，总变化不到基期的 0.5% 算基本持平，各段之和要和总数对得上才判断变化来自哪里",
+      "verdict": {
+       "code": "driver",
+       "direction": "down",
+       "subjects": [
+        [
+         "华东"
+        ]
+       ]
+      },
+      "caveats": [
+       {
+        "code": "comparability",
+        "text": "没能核对本期的数据是否到齐（期次写成「年-W周号」，认不出 SQL 是按哪种周号算的，不知道是哪几天）"
+       }
+      ],
+      "hypothesis": "下降是不是集中在某个地区",
+      "hypothesis_note": "Agent 写的假设：说明这次在验证什么，不是分析结论，也不进报告正文"
+     },
+     {
+      "step": "analysis_method",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "method_label": "变化拆解（可加）",
+      "params": {
+       "base": "2026-W41",
+       "by": [
+        "region"
+       ],
+       "catalog": {
+        "labels": {
+         "region": "地区",
+         "week": "周"
+        },
+        "measures": {
+         "gmv": "flow"
+        }
+       },
+       "headline": {
+        "base": "Q1.r0.gmv",
+        "target": "Q1.r1.gmv"
+       },
+       "label": "销售额",
+       "measure": "gmv",
+       "period": "week",
+       "query": "Q2",
+       "target": "2026-W42",
+       "today": "2026-10-21"
+      },
+      "settings": [
+       {
+        "key": "main_share",
+        "label": "「主要来源」的占比",
+        "value": 0.5
+       },
+       {
+        "key": "concentration",
+        "label": "「集中」的合计占比",
+        "value": 0.8
+       },
+       {
+        "key": "offset_ratio",
+        "label": "记为「抵消」的比例",
+        "value": 0.25
+       },
+       {
+        "key": "flat_pct",
+        "label": "「基本持平」的幅度",
+        "value": 0.005
+       },
+       {
+        "key": "table_top_k",
+        "label": "列出前几段",
+        "value": 10
+       }
+      ],
+      "probes": []
+     },
+     {
+      "step": "analysis_calc",
+      "alias": "A1",
+      "values": [
+       {
+        "key": "base_total",
+        "raw": "2700",
+        "rendered": "2,700"
+       },
+       {
+        "key": "delta_total",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_down",
+        "raw": "-340",
+        "rendered": "-340"
+       },
+       {
+        "key": "gross_up",
+        "raw": "0",
+        "rendered": "0"
+       },
+       {
+        "key": "pct_total",
+        "raw": "-0.125925925926",
+        "rendered": "-12.6%"
+       },
+       {
+        "key": "segments",
+        "raw": "4",
+        "rendered": "4"
+       },
+       {
+        "key": "target_total",
+        "raw": "2360",
+        "rendered": "2,360"
+       },
+       {
+        "key": "top_k",
+        "raw": "1",
+        "rendered": "1"
+       },
+       {
+        "key": "top_k_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       },
+       {
+        "key": "top_key",
+        "raw": "华东",
+        "rendered": "华东"
+       },
+       {
+        "key": "top_share",
+        "raw": "0.882352941176",
+        "rendered": "88.2%"
+       }
+      ],
+      "formulas": [
+       {
+        "label": "总变化",
+        "text": "总变化 = 本期合计 − 基期合计 = 2,360 − 2,700 = -340"
+       },
+       {
+        "label": "变化率",
+        "text": "变化率 = 总变化 ÷ 基期合计 = -340 ÷ 2,700 = -12.6%"
+       },
+       {
+        "label": "占比",
+        "text": "华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%"
+       }
+      ],
+      "table": {
+       "columns": [
+        "region",
+        "base",
+        "target",
+        "delta",
+        "pct",
+        "share",
+        "flag"
+       ],
+       "labels": {
+        "base": "基期",
+        "delta": "变化",
+        "flag": "说明",
+        "pct": "变化率",
+        "region": "地区",
+        "share": "占变化的比重",
+        "target": "本期"
+       },
+       "total_rows": 4,
+       "row_index": [
+        0,
+        1,
+        2,
+        3
+       ],
+       "rows": [
+        [
+         "华东",
+         "1,000",
+         "700",
+         "-300",
+         "-30.0%",
+         "88.2%",
+         "—"
+        ],
+        [
+         "华南",
+         "800",
+         "780",
+         "-20",
+         "-2.5%",
+         "5.9%",
+         "—"
+        ],
+        [
+         "华北",
+         "600",
+         "590",
+         "-10",
+         "-1.7%",
+         "2.9%",
+         "—"
+        ],
+        [
+         "西南",
+         "300",
+         "290",
+         "-10",
+         "-3.3%",
+         "2.9%",
+         "—"
+        ]
+       ],
+       "masked": [],
+       "highlight": {
+        "cells": [
+         [
+          1,
+          "share"
+         ]
+        ]
+       }
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r0.gmv",
+      "role": "headline_base",
+      "role_label": "基期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 0,
+      "column": "gmv",
+      "value": "2700",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "base_total",
+       "total": "2,700",
+       "headline": "2,700",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q1.r1.gmv",
+      "role": "headline_target",
+      "role_label": "本期总数",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "row": 1,
+      "column": "gmv",
+      "value": "2360",
+      "query": "Q1",
+      "node_id": "bot",
+      "sealed": true,
+      "closure": {
+       "mode": "additive",
+       "total_key": "target_total",
+       "total": "2,360",
+       "headline": "2,360",
+       "closed": true
+      }
+     },
+     {
+      "step": "analysis_input",
+      "of": "A1",
+      "ref": "Q2",
+      "role": "data",
+      "role_label": "数据",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": "all",
+      "query": "Q2",
+      "node_id": "bot",
+      "sealed": true
+     },
+     {
+      "step": "query",
+      "alias": "Q1",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT week, SUM(amount) AS gmv FROM orders GROUP BY week ORDER BY week",
+      "columns": [
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "2026-W41",
+        2700
+       ],
+       [
+        "2026-W42",
+        2360
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1
+      ],
+      "total_rows": 2,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1
+       ],
+       "cols": [
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "query",
+      "alias": "Q2",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
+      "node_id": "bot",
+      "tool": "db_query__shop",
+      "source": "shop",
+      "sql": "SELECT region, week, SUM(amount) AS gmv FROM orders GROUP BY region, week ORDER BY region, week",
+      "columns": [
+       "region",
+       "week",
+       "gmv"
+      ],
+      "rows": [
+       [
+        "华东",
+        "2026-W41",
+        1000
+       ],
+       [
+        "华东",
+        "2026-W42",
+        700
+       ],
+       [
+        "华北",
+        "2026-W41",
+        600
+       ],
+       [
+        "华北",
+        "2026-W42",
+        590
+       ],
+       [
+        "华南",
+        "2026-W41",
+        800
+       ],
+       [
+        "华南",
+        "2026-W42",
+        780
+       ],
+       [
+        "西南",
+        "2026-W41",
+        300
+       ],
+       [
+        "西南",
+        "2026-W42",
+        290
+       ]
+      ],
+      "row_offset": 0,
+      "row_index": [
+       0,
+       1,
+       2,
+       3,
+       4,
+       5,
+       6,
+       7
+      ],
+      "total_rows": 8,
+      "truncated": false,
+      "highlight": {
+       "rows": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7
+       ],
+       "cols": [
+        "region",
+        "week",
+        "gmv"
+       ],
+       "cells": [
+        [
+         0,
+         "region"
+        ],
+        [
+         1,
+         "region"
+        ],
+        [
+         2,
+         "region"
+        ],
+        [
+         3,
+         "region"
+        ],
+        [
+         4,
+         "region"
+        ],
+        [
+         5,
+         "region"
+        ],
+        [
+         6,
+         "region"
+        ],
+        [
+         7,
+         "region"
+        ],
+        [
+         0,
+         "week"
+        ],
+        [
+         1,
+         "week"
+        ],
+        [
+         2,
+         "week"
+        ],
+        [
+         3,
+         "week"
+        ],
+        [
+         4,
+         "week"
+        ],
+        [
+         5,
+         "week"
+        ],
+        [
+         6,
+         "week"
+        ],
+        [
+         7,
+         "week"
+        ],
+        [
+         0,
+         "gmv"
+        ],
+        [
+         1,
+         "gmv"
+        ],
+        [
+         2,
+         "gmv"
+        ],
+        [
+         3,
+         "gmv"
+        ],
+        [
+         4,
+         "gmv"
+        ],
+        [
+         5,
+         "gmv"
+        ],
+        [
+         6,
+         "gmv"
+        ],
+        [
+         7,
+         "gmv"
+        ]
+       ]
+      },
+      "masked": [],
+      "hash_ok": true,
+      "sealed": true,
+      "column_types": {
+       "gmv": "number",
+       "region": "text",
+       "week": "text"
+      },
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
+      "catalog": [
+       {
+        "table": "orders",
+        "version": 1
+       }
+      ],
+      "input_of": "A1"
+     },
+     {
+      "step": "analysis_recompute",
+      "alias": "A1",
+      "method": "decompose.additive@1",
+      "status": "same",
+      "text": "复算一致：按封存的输入重新计算，结论逐项相同"
+     },
+     {
+      "step": "analysis_seal",
+      "alias": "A1",
+      "node_id": "bot",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "ledger": true,
+      "hash_ok": true,
+      "eid_ok": true,
+      "sealed": true
+     }
+    ],
+    "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写",
+    "violations": [],
+    "seal": {
+     "sealed": true,
+     "ok": true,
+     "covered": true
+    },
+    "redacted": {
+     "columns": []
+    }
    }
   },
   "graph": {
-   "run_id": "ed3de345aac143089fb3d0777d9056ec",
+   "run_id": "6d4d8c841a7d4900a3495f24bc932afa",
    "schema": "agentlab.evidence/1",
    "seal": {
     "sealed": true,
     "ok": true,
-    "manifest_seq": 78,
+    "manifest_seq": 84,
     "legacy": false
    },
    "reports": [
     {
      "node_id": "write",
-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc",
      "doc_sealed": true,
      "hash_ok": true,
      "ok": false,
@@ -2786,22 +10762,22 @@
      ],
      "stats": {
       "analysis_claims": 3,
-      "claims": 5,
-      "connective": 0,
+      "claims": 14,
+      "connective": 4,
       "entities": 0,
       "headings": 1,
-      "numbers": 2,
-      "numbers_cited": 2,
+      "numbers": 11,
+      "numbers_cited": 11,
       "quotes": 0,
       "see": 2,
-      "segments": 17,
+      "segments": 50,
       "uncited_claims": 2,
       "uncited_numbers": 0,
-      "units": 6,
+      "units": 19,
       "unknown_entities": 0,
       "unresolved": 0,
       "unverified_entities": 0,
-      "values": 1,
+      "values": 3,
       "violations": 3
      },
      "claims": "off",
@@ -2813,21 +10789,41 @@
    "evidence": [
     {
      "alias": "A1",
-     "eid": "ev:analysis:10fb652eb3e403a8",
+     "eid": "ev:analysis:a7edbc13c92c31a0",
      "kind": "analysis",
      "label": "变化拆解（可加） · 销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
      "node_id": "bot",
-     "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+     "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
      "sealed": true,
      "cited_by": [
       "s3",
       "s7",
-      "s9"
+      "s9",
+      "s19",
+      "s21",
+      "s23",
+      "s34",
+      "s36",
+      "s38",
+      "s40",
+      "s42",
+      "s44",
+      "s46",
+      "s48"
      ],
      "units": [
       "u1",
       "u2",
-      "u3"
+      "u3",
+      "u6",
+      "u11",
+      "u12",
+      "u13",
+      "u14",
+      "u15",
+      "u16",
+      "u17",
+      "u18"
      ],
      "report": "write",
      "op": "decompose",
@@ -2838,11 +10834,11 @@
     },
     {
      "alias": "A2",
-     "eid": "ev:analysis:436f6b967aa1414c",
+     "eid": "ev:analysis:d89ecaae59a168f0",
      "kind": "analysis",
      "label": "波动基线 · 波动基线没有做成：只有 0 期历史，至少要 8 期",
      "node_id": "bot",
-     "artifact": "2e4139852d4ca46f8ee328ce44cb2ddde14f79e54c1371b3a77e15bf232b0122",
+     "artifact": "edaa673d3f04f17f8968ef35209c2ad6b281a9d54de71e383bed7df318ec5d99",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -2853,11 +10849,11 @@
     },
     {
      "alias": "A3",
-     "eid": "ev:analysis:bb168f2c30497439",
+     "eid": "ev:analysis:066e85d56f331b63",
      "kind": "analysis",
      "label": "可比性检查 · 两期可比，窗口长度、整期、星期构成核对不了",
      "node_id": "bot",
-     "artifact": "afa2ea2ba75e5eb20e7f3372e3a9dea164eddb0338fa51c6b2fd9a808c09072a",
+     "artifact": "df87e1917658e653f79ccac7e43e2cf70210b04dc2194bb849eb099303b66f23",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -2869,11 +10865,11 @@
     },
     {
      "alias": "Q1",
-     "eid": "ev:query:ad723c3d0c0ae28c",
+     "eid": "ev:query:9c920fc7352dde33",
      "kind": "query",
      "label": "db_query__shop · 2 行",
      "node_id": "bot",
-     "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+     "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -2889,11 +10885,11 @@
     },
     {
      "alias": "Q2",
-     "eid": "ev:query:c390b8b187033425",
+     "eid": "ev:query:42ab65ce25770550",
      "kind": "query",
      "label": "db_query__shop · 8 行",
      "node_id": "bot",
-     "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+     "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -2940,6 +10936,72 @@
      "rel": "supports",
      "report": "write"
     },
+    {
+     "from": "s19",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s21",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s23",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s34",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s36",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s38",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s40",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s42",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s44",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s46",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
+    {
+     "from": "s48",
+     "to": "A1",
+     "rel": "cites",
+     "report": "write"
+    },
     {
      "from": "A1",
      "to": "Q1",
@@ -2969,18 +11031,60 @@
      "to": "A1",
      "rel": "cell_of",
      "report": "write"
+    },
+    {
+     "from": "A1.r0.pct",
+     "to": "A1",
+     "rel": "cell_of",
+     "report": "write"
+    },
+    {
+     "from": "A1.r0.share",
+     "to": "A1",
+     "rel": "cell_of",
+     "report": "write"
+    },
+    {
+     "from": "A1.r0.region",
+     "to": "A1",
+     "rel": "cell_of",
+     "report": "write"
+    },
+    {
+     "from": "A1.r1.region",
+     "to": "A1",
+     "rel": "cell_of",
+     "report": "write"
+    },
+    {
+     "from": "A1.r1.delta",
+     "to": "A1",
+     "rel": "cell_of",
+     "report": "write"
+    },
+    {
+     "from": "A1.r1.pct",
+     "to": "A1",
+     "rel": "cell_of",
+     "report": "write"
+    },
+    {
+     "from": "A1.r1.share",
+     "to": "A1",
+     "rel": "cell_of",
+     "report": "write"
     }
    ],
    "mode": "cited"
   },
   "audit": {
-   "run_id": "ed3de345aac143089fb3d0777d9056ec",
+   "run_id": "6d4d8c841a7d4900a3495f24bc932afa",
    "schema": "agentlab.evidence.audit/1",
    "mode": "cited",
    "seal": {
     "sealed": true,
     "ok": true,
-    "manifest_seq": 78,
+    "manifest_seq": 84,
     "legacy": false,
     "events": 45,
     "message": "事件记录与封存时一致"
@@ -2988,7 +11092,7 @@
    "reports": [
     {
      "node_id": "write",
-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389",
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc",
      "hash_ok": true,
      "doc_sealed": true,
      "fields": [
@@ -2996,22 +11100,22 @@
      ],
      "stats": {
       "analysis_claims": 3,
-      "claims": 5,
-      "connective": 0,
+      "claims": 14,
+      "connective": 4,
       "entities": 0,
       "headings": 1,
-      "numbers": 2,
-      "numbers_cited": 2,
+      "numbers": 11,
+      "numbers_cited": 11,
       "quotes": 0,
       "see": 2,
-      "segments": 17,
+      "segments": 50,
       "uncited_claims": 2,
       "uncited_numbers": 0,
-      "units": 6,
+      "units": 19,
       "unknown_entities": 0,
       "unresolved": 0,
       "unverified_entities": 0,
-      "values": 1,
+      "values": 3,
       "violations": 3
      },
      "claims": "off",
@@ -3024,7 +11128,7 @@
     {
      "key": "cited",
      "label": "有出处",
-     "count": 3,
+     "count": 14,
      "rows": [
       {
        "group": "cited",
@@ -3040,8 +11144,8 @@
        "alias": "A1",
        "evidence_kind": "analysis",
        "evidence": "变化拆解（可加） · 销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
-       "eid": "ev:analysis:10fb652eb3e403a8",
-       "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+       "eid": "ev:analysis:a7edbc13c92c31a0",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
        "node_id": "bot",
        "sealed": true,
        "span": [
@@ -3065,8 +11169,8 @@
        "alias": "A1",
        "evidence_kind": "analysis",
        "evidence": "A1.r0.delta",
-       "eid": "ev:cell:164abd36aeac4c3b",
-       "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+       "eid": "ev:cell:a7deaec3f4ed6ab6",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
        "node_id": "bot",
        "sealed": true,
        "span": [
@@ -3090,8 +11194,8 @@
        "alias": "A1",
        "evidence_kind": "analysis",
        "evidence": "A1.top_share",
-       "eid": "ev:analysis:efc6267321553bc3",
-       "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+       "eid": "ev:analysis:5517f6de3df56204",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
        "node_id": "bot",
        "sealed": true,
        "span": [
@@ -3100,6 +11204,281 @@
        ],
        "sentence": "华东的变化为 -300，占总变化的 88.2%。",
        "note": "这个数取自分析结论的具名的数，由系统按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s19",
+       "unit": "u6",
+       "kind": "number",
+       "text": "-30.0%",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r0.pct",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r0.pct",
+       "eid": "ev:cell:32d18640349e7717",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        121,
+        127
+       ],
+       "sentence": "变化率 -30.0%、比重 88.2%、占比 88.2%。",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s21",
+       "unit": "u6",
+       "kind": "number",
+       "text": "88.2%",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r0.share",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r0.share",
+       "eid": "ev:cell:b36848a6055a5628",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        131,
+        136
+       ],
+       "sentence": "变化率 -30.0%、比重 88.2%、占比 88.2%。",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s23",
+       "unit": "u6",
+       "kind": "number",
+       "text": "88.2%",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "a:A1.top_share",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.top_share",
+       "eid": "ev:analysis:5517f6de3df56204",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        140,
+        145
+       ],
+       "sentence": "变化率 -30.0%、比重 88.2%、占比 88.2%。",
+       "note": "这个数取自分析结论的具名的数，由系统按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s34",
+       "unit": "u11",
+       "kind": "value",
+       "text": "华东",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r0.region",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r0.region",
+       "eid": "ev:cell:cf44ef048f0ff6cf",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        203,
+        205
+       ],
+       "sentence": "华东",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s36",
+       "unit": "u12",
+       "kind": "number",
+       "text": "-300",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r0.delta",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r0.delta",
+       "eid": "ev:cell:a7deaec3f4ed6ab6",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        208,
+        212
+       ],
+       "sentence": "-300",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s38",
+       "unit": "u13",
+       "kind": "number",
+       "text": "-30.0%",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r0.pct",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r0.pct",
+       "eid": "ev:cell:32d18640349e7717",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        215,
+        221
+       ],
+       "sentence": "-30.0%",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s40",
+       "unit": "u14",
+       "kind": "number",
+       "text": "88.2%",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r0.share",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r0.share",
+       "eid": "ev:cell:b36848a6055a5628",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        224,
+        229
+       ],
+       "sentence": "88.2%",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s42",
+       "unit": "u15",
+       "kind": "value",
+       "text": "华南",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r1.region",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r1.region",
+       "eid": "ev:cell:15977323c7fb25b1",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        234,
+        236
+       ],
+       "sentence": "华南",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s44",
+       "unit": "u16",
+       "kind": "number",
+       "text": "-20",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r1.delta",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r1.delta",
+       "eid": "ev:cell:50fa5438a057327a",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        239,
+        242
+       ],
+       "sentence": "-20",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s46",
+       "unit": "u17",
+       "kind": "number",
+       "text": "-2.5%",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r1.pct",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r1.pct",
+       "eid": "ev:cell:1bb9cb78a05f83a2",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        245,
+        250
+       ],
+       "sentence": "-2.5%",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
+      },
+      {
+       "group": "cited",
+       "report": "write",
+       "field": "周报",
+       "segment": "s48",
+       "unit": "u18",
+       "kind": "number",
+       "text": "5.9%",
+       "state": "deterministic",
+       "issue": null,
+       "ref": "v:A1.r1.share",
+       "alias": "A1",
+       "evidence_kind": "analysis",
+       "evidence": "A1.r1.share",
+       "eid": "ev:cell:379dcf4b1b6947b1",
+       "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+       "node_id": "bot",
+       "sealed": true,
+       "span": [
+        253,
+        257
+       ],
+       "sentence": "5.9%",
+       "note": "该值由系统从分析结论的结果表中取出、按固定规则显示，未经模型转写"
       }
      ]
     },
@@ -3249,19 +11628,19 @@
     }
    ],
    "counts": {
-    "cited": 3,
+    "cited": 14,
     "none": 5,
     "suspicious": 0,
     "candidate": 0
    },
-   "total": 8
+   "total": 19
   },
   "analyses": {
    "A1": {
-    "run_id": "ed3de345aac143089fb3d0777d9056ec",
+    "run_id": "6d4d8c841a7d4900a3495f24bc932afa",
     "report": {
      "node_id": "write",
-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
     },
     "alias": "A1",
     "rendered": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
@@ -3276,8 +11655,8 @@
       "call_id": "call_3",
       "node_id": "bot",
       "label": "问数据",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
-      "eid": "ev:analysis:10fb652eb3e403a8",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
+      "eid": "ev:analysis:a7edbc13c92c31a0",
       "eid_ok": true,
       "cited": {
        "what": "phrase"
@@ -3395,7 +11774,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
        },
        {
         "key": "segments",
@@ -3415,7 +11794,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        },
        {
         "key": "top_key",
@@ -3425,7 +11804,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        }
       ],
       "formulas": [
@@ -3471,39 +11850,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
         ],
         [
          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
         ],
         [
          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
         ],
         [
          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
         ]
        ],
        "masked": [],
@@ -3518,7 +11897,7 @@
       "ref": "Q1.r0.gmv",
       "role": "headline_base",
       "role_label": "基期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "row": 0,
       "column": "gmv",
       "value": "2700",
@@ -3540,7 +11919,7 @@
       "ref": "Q1.r1.gmv",
       "role": "headline_target",
       "role_label": "本期总数",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -3562,7 +11941,7 @@
       "ref": "Q2",
       "role": "data",
       "role_label": "数据",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "columns": [
        "region",
        "week",
@@ -3576,7 +11955,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -3628,7 +12007,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -3640,7 +12019,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -3828,7 +12207,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -3848,7 +12227,7 @@
       "step": "analysis_seal",
       "alias": "A1",
       "node_id": "bot",
-      "artifact": "6d06b2913312497d982d4e36e0c94a57c377b990110bac960d72d64979d7c12d",
+      "artifact": "f5d1fc01d62f6e37c62984318f0cb354f47ecb7ab30e7565222afaff49ee4f91",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -3865,10 +12244,10 @@
     }
    },
    "A2": {
-    "run_id": "ed3de345aac143089fb3d0777d9056ec",
+    "run_id": "6d4d8c841a7d4900a3495f24bc932afa",
     "report": {
      "node_id": "write",
-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
     },
     "alias": "A2",
     "rendered": "波动基线没有做成：只有 0 期历史，至少要 8 期",
@@ -3883,8 +12262,8 @@
       "call_id": "call_4",
       "node_id": "bot",
       "label": "问数据",
-      "artifact": "2e4139852d4ca46f8ee328ce44cb2ddde14f79e54c1371b3a77e15bf232b0122",
-      "eid": "ev:analysis:436f6b967aa1414c",
+      "artifact": "edaa673d3f04f17f8968ef35209c2ad6b281a9d54de71e383bed7df318ec5d99",
+      "eid": "ev:analysis:d89ecaae59a168f0",
       "eid_ok": true,
       "cited": {
        "what": "phrase"
@@ -3955,7 +12334,7 @@
       "ref": "Q1",
       "role": "data",
       "role_label": "数据",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "columns": [
        "week",
        "gmv"
@@ -3968,7 +12347,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "8fa60a625e4a072804462dd66dde6b1634f9fbd8244183e626a6fdcd8acb87fc",
+      "artifact": "4660b212dfdeb2fc6ce8ffe74562f14ebe48bbdbe4152e987e43482a343a47f3",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -4029,7 +12408,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -4049,7 +12428,7 @@
       "step": "analysis_seal",
       "alias": "A2",
       "node_id": "bot",
-      "artifact": "2e4139852d4ca46f8ee328ce44cb2ddde14f79e54c1371b3a77e15bf232b0122",
+      "artifact": "edaa673d3f04f17f8968ef35209c2ad6b281a9d54de71e383bed7df318ec5d99",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -4066,10 +12445,10 @@
     }
    },
    "A3": {
-    "run_id": "ed3de345aac143089fb3d0777d9056ec",
+    "run_id": "6d4d8c841a7d4900a3495f24bc932afa",
     "report": {
      "node_id": "write",
-     "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+     "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
     },
     "alias": "A3",
     "rendered": "两期可比，窗口长度、整期、星期构成核对不了",
@@ -4084,8 +12463,8 @@
       "call_id": "call_5",
       "node_id": "bot",
       "label": "问数据",
-      "artifact": "afa2ea2ba75e5eb20e7f3372e3a9dea164eddb0338fa51c6b2fd9a808c09072a",
-      "eid": "ev:analysis:bb168f2c30497439",
+      "artifact": "df87e1917658e653f79ccac7e43e2cf70210b04dc2194bb849eb099303b66f23",
+      "eid": "ev:analysis:066e85d56f331b63",
       "eid_ok": true,
       "cited": {
        "what": "phrase"
@@ -4188,37 +12567,37 @@
         [
          "同口径",
          "通过",
-         null,
-         null,
-         null
+         "—",
+         "—",
+         "—"
         ],
         [
          "窗口长度",
          "核对不了",
-         null,
-         null,
-         null
+         "—",
+         "—",
+         "—"
         ],
         [
          "整期",
          "核对不了",
-         null,
-         null,
-         null
+         "—",
+         "—",
+         "—"
         ],
         [
          "星期构成",
          "核对不了",
-         null,
-         null,
-         null
+         "—",
+         "—",
+         "—"
         ],
         [
          "数据问题",
          "通过",
-         null,
-         null,
-         null
+         "—",
+         "—",
+         "—"
         ]
        ],
        "masked": [],
@@ -4233,7 +12612,7 @@
       "ref": "Q2",
       "role": "data",
       "role_label": "数据",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "columns": [
        "week",
        "gmv"
@@ -4246,7 +12625,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "94f47ca377dfd4fddc3e4bdba0466cbefdd6c4b66beaf4468332a9dd51be7901",
+      "artifact": "8322aa7cc7c2ad7d63887acb8d88d34ef6b1a9ed948afdb3acc9f7bd5328f460",
       "node_id": "bot",
       "tool": "db_query__shop",
       "source": "shop",
@@ -4401,7 +12780,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -4421,7 +12800,7 @@
       "step": "analysis_seal",
       "alias": "A3",
       "node_id": "bot",
-      "artifact": "afa2ea2ba75e5eb20e7f3372e3a9dea164eddb0338fa51c6b2fd9a808c09072a",
+      "artifact": "df87e1917658e653f79ccac7e43e2cf70210b04dc2194bb849eb099303b66f23",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -4443,7 +12822,7 @@
    "tier": "formal",
    "report": {
     "node_id": "write",
-    "doc_artifact": "d115c361e8ae4f6d06bd4f839c6f6f6c62c0721575b048da7350834f8ce41389"
+    "doc_artifact": "32cc0cc3a309ef588a619cbf69d277710b6c0505b9c36ca880b50feaebca63dc"
    },
    "calibers": [],
    "metrics_checked": 0,
@@ -4451,7 +12830,7 @@
    "missing_expected": [],
    "unmatched_numbers": [],
    "unresolved": [],
-   "matched_numbers": 2,
+   "matched_numbers": 11,
    "matched": [
     {
      "token": "-300",
@@ -4465,7 +12844,7 @@
       57,
       61
      ],
-     "eid": "ev:cell:164abd36aeac4c3b"
+     "eid": "ev:cell:a7deaec3f4ed6ab6"
     },
     {
      "token": "88.2%",
@@ -4479,18 +12858,144 @@
       68,
       73
      ],
-     "eid": "ev:analysis:efc6267321553bc3"
+     "eid": "ev:analysis:5517f6de3df56204"
+    },
+    {
+     "token": "-30.0%",
+     "metric": null,
+     "cell": "A1.r0.pct",
+     "segment": "s19",
+     "unit": "u6",
+     "start": 121,
+     "end": 127,
+     "span": [
+      121,
+      127
+     ],
+     "eid": "ev:cell:32d18640349e7717"
+    },
+    {
+     "token": "88.2%",
+     "metric": null,
+     "cell": "A1.r0.share",
+     "segment": "s21",
+     "unit": "u6",
+     "start": 131,
+     "end": 136,
+     "span": [
+      131,
+      136
+     ],
+     "eid": "ev:cell:b36848a6055a5628"
+    },
+    {
+     "token": "88.2%",
+     "metric": null,
+     "analysis": "A1.top_share",
+     "segment": "s23",
+     "unit": "u6",
+     "start": 140,
+     "end": 145,
+     "span": [
+      140,
+      145
+     ],
+     "eid": "ev:analysis:5517f6de3df56204"
+    },
+    {
+     "token": "-300",
+     "metric": null,
+     "cell": "A1.r0.delta",
+     "segment": "s36",
+     "unit": "u12",
+     "start": 208,
+     "end": 212,
+     "span": [
+      208,
+      212
+     ],
+     "eid": "ev:cell:a7deaec3f4ed6ab6"
+    },
+    {
+     "token": "-30.0%",
+     "metric": null,
+     "cell": "A1.r0.pct",
+     "segment": "s38",
+     "unit": "u13",
+     "start": 215,
+     "end": 221,
+     "span": [
+      215,
+      221
+     ],
+     "eid": "ev:cell:32d18640349e7717"
+    },
+    {
+     "token": "88.2%",
+     "metric": null,
+     "cell": "A1.r0.share",
+     "segment": "s40",
+     "unit": "u14",
+     "start": 224,
+     "end": 229,
+     "span": [
+      224,
+      229
+     ],
+     "eid": "ev:cell:b36848a6055a5628"
+    },
+    {
+     "token": "-20",
+     "metric": null,
+     "cell": "A1.r1.delta",
+     "segment": "s44",
+     "unit": "u16",
+     "start": 239,
+     "end": 242,
+     "span": [
+      239,
+      242
+     ],
+     "eid": "ev:cell:50fa5438a057327a"
+    },
+    {
+     "token": "-2.5%",
+     "metric": null,
+     "cell": "A1.r1.pct",
+     "segment": "s46",
+     "unit": "u17",
+     "start": 245,
+     "end": 250,
+     "span": [
+      245,
+      250
+     ],
+     "eid": "ev:cell:1bb9cb78a05f83a2"
+    },
+    {
+     "token": "5.9%",
+     "metric": null,
+     "cell": "A1.r1.share",
+     "segment": "s48",
+     "unit": "u18",
+     "start": 253,
+     "end": 257,
+     "span": [
+      253,
+      257
+     ],
+     "eid": "ev:cell:379dcf4b1b6947b1"
     }
    ],
    "stats": {
-    "units": 6,
-    "segments": 17,
-    "claims": 5,
-    "connective": 0,
+    "units": 19,
+    "segments": 50,
+    "claims": 14,
+    "connective": 4,
     "headings": 1,
-    "numbers": 2,
-    "numbers_cited": 2,
-    "values": 1,
+    "numbers": 11,
+    "numbers_cited": 11,
+    "values": 3,
     "entities": 0,
     "quotes": 0,
     "uncited_numbers": 0,
@@ -4503,7 +13008,7 @@
     "analysis_claims": 3
    },
    "gaps": [],
-   "declared_at": "2026-10-21T10:36:36.123894+00:00",
+   "declared_at": "2026-10-21T15:22:24.686033+00:00",
    "analysis_claims": {
     "mode": "check",
     "count": 3,
@@ -4563,9 +13068,9 @@
   }
  },
  "card": {
-  "run_id": "ffeb7bfbcde6480fbddb573a6c6558af",
+  "run_id": "803b3fd6fc66470b8d0c8353cfd87899",
   "run_class": "exploratory",
-  "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75",
+  "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9",
   "doc": {
    "analysis_claims": {
     "lexicon": 1,
@@ -4660,7 +13165,7 @@
        "see": [
         {
          "alias": "A2",
-         "eid": "ev:analysis:b11cf44f63eaa443",
+         "eid": "ev:analysis:f872ae8bf6eeaee1",
          "kind": "analysis",
          "locator": {},
          "ref": "A2",
@@ -4682,7 +13187,7 @@
         {
          "cite": {
           "alias": "A2",
-          "eid": "ev:analysis:b11cf44f63eaa443",
+          "eid": "ev:analysis:f872ae8bf6eeaee1",
           "kind": "analysis",
           "locator": {},
           "ref": "A2",
@@ -4743,7 +13248,7 @@
         {
          "cite": {
           "alias": "A1",
-          "eid": "ev:analysis:88367707fd963815",
+          "eid": "ev:analysis:faa7de010775194d",
           "kind": "analysis",
           "locator": {},
           "ref": "A1",
@@ -4804,7 +13309,7 @@
         {
          "cite": {
           "alias": "A2",
-          "eid": "ev:cell:8f04cbf82264a3bb",
+          "eid": "ev:cell:289f04710d52804d",
           "kind": "cell",
           "locator": {
            "column": "delta",
@@ -4981,7 +13486,7 @@
         {
          "cite": {
           "alias": "A2",
-          "eid": "ev:cell:8f04cbf82264a3bb",
+          "eid": "ev:cell:289f04710d52804d",
           "kind": "cell",
           "locator": {
            "column": "delta",
@@ -5025,11 +13530,11 @@
    "catalog": {
     "A1": {
      "alias": "A1",
-     "artifact": "c3ab06e1330c51f0567f410f6275a2726a38cad2bd092e145dc6c70260545e9b",
-     "eid": "ev:analysis:88367707fd963815",
+     "artifact": "a234a885f55bde88562ab83886da7a3f2c60f9cc352ce05ca6481d32166886f0",
+     "eid": "ev:analysis:faa7de010775194d",
      "exec": 1,
      "inputs": [
-      "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e"
+      "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76"
      ],
      "item": "wow",
      "kind": "analysis",
@@ -5043,12 +13548,12 @@
     },
     "A2": {
      "alias": "A2",
-     "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
-     "eid": "ev:analysis:b11cf44f63eaa443",
+     "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
+     "eid": "ev:analysis:f872ae8bf6eeaee1",
      "exec": 1,
      "inputs": [
-      "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
-      "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9"
+      "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
+      "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce"
      ],
      "item": "where",
      "kind": "analysis",
@@ -5072,12 +13577,12 @@
     },
     "Q1": {
      "alias": "Q1",
-     "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+     "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
      "columns": [
       "week",
       "gmv"
      ],
-     "eid": "ev:query:30fcabb3fa33690b",
+     "eid": "ev:query:1697ecefc4f957ae",
      "exec": 1,
      "kind": "query",
      "label": "db_query__shop · 2 行",
@@ -5087,17 +13592,17 @@
      "source": "shop",
      "tool": "db_query__shop",
      "truncated": false,
-     "via": "b1dacecebc489f096e7582673486790ee51a258afc93144289992bfa7f9eb59d"
+     "via": "8c92a414da25b414b17fdadd1b9e2e2f767d20f0e9e788561d509af73046cc05"
     },
     "Q2": {
      "alias": "Q2",
-     "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+     "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
      "columns": [
       "region",
       "week",
       "gmv"
      ],
-     "eid": "ev:query:d3b62a2e9c2b52a7",
+     "eid": "ev:query:9edc88bec38d5ca5",
      "exec": 1,
      "kind": "query",
      "label": "db_query__shop · 8 行",
@@ -5107,14 +13612,14 @@
      "source": "shop",
      "tool": "db_query__shop",
      "truncated": false,
-     "via": "452f5b41122353bb5a478d4ba092e8321eb371edced6dbc6ccd0c920291eff54"
+     "via": "e376922d642ba221ce1dd5d6a4e3f14735e0bdeab37e051924446564f1e7fa26"
     }
    },
    "entity_syntax": 2,
    "markdown": "## 本周销售\n\n销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）。\n\n波动基线没有做成：只有 0 期历史，至少要 8 期\n\n华东的变化为 -300。\n\n| 地区 | 变化 |\n|---|---|\n| 华东 | -300 |",
    "node_id": "write",
    "provenance": 1,
-   "run_id": "ffeb7bfbcde6480fbddb573a6c6558af",
+   "run_id": "803b3fd6fc66470b8d0c8353cfd87899",
    "schema": "agentlab.report/1",
    "source": "## 本周销售\n\n[[a:A2]]。[[see:A2]]\n\n[[a:A1]]\n\n华东的变化为 [[v:A2.r0.delta]]。\n\n| 地区 | 变化 |\n|---|---|\n| 华东 | [[v:A2.r0.delta]] |",
    "stats": {
@@ -5143,7 +13648,7 @@
    "s3": {
     "report": {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75"
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9"
     },
     "segment": {
      "id": "s3",
@@ -5158,7 +13663,7 @@
      "ref": "a:A2",
      "cite": {
       "alias": "A2",
-      "eid": "ev:analysis:b11cf44f63eaa443",
+      "eid": "ev:analysis:f872ae8bf6eeaee1",
       "kind": "analysis",
       "locator": {},
       "ref": "A2",
@@ -5199,8 +13704,8 @@
       "item": "where",
       "node_id": "card",
       "label": "异动分析",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
-      "eid": "ev:analysis:b11cf44f63eaa443",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
+      "eid": "ev:analysis:f872ae8bf6eeaee1",
       "eid_ok": true,
       "cited": {
        "what": "phrase"
@@ -5318,7 +13823,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
        },
        {
         "key": "segments",
@@ -5338,7 +13843,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        },
        {
         "key": "top_key",
@@ -5348,7 +13853,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        }
       ],
       "formulas": [
@@ -5394,39 +13899,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
         ],
         [
          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
         ],
         [
          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
         ],
         [
          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
         ]
        ],
        "masked": [],
@@ -5441,7 +13946,7 @@
       "ref": "weekly.r0.gmv",
       "role": "headline_base",
       "role_label": "基期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 0,
       "column": "gmv",
       "value": "2700",
@@ -5463,7 +13968,7 @@
       "ref": "weekly.r1.gmv",
       "role": "headline_target",
       "role_label": "本期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -5485,7 +13990,7 @@
       "ref": "by_region",
       "role": "data",
       "role_label": "数据",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "columns": [
        "region",
        "week",
@@ -5499,7 +14004,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "node_id": "weekly",
       "tool": "db_query__shop",
       "source": "shop",
@@ -5551,7 +14056,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -5563,7 +14068,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "node_id": "by_region",
       "tool": "db_query__shop",
       "source": "shop",
@@ -5751,7 +14256,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -5771,7 +14276,7 @@
       "step": "analysis_seal",
       "alias": "A2",
       "node_id": "card",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -5792,7 +14297,7 @@
    "s6": {
     "report": {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75"
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9"
     },
     "segment": {
      "id": "s6",
@@ -5807,7 +14312,7 @@
      "ref": "a:A1",
      "cite": {
       "alias": "A1",
-      "eid": "ev:analysis:88367707fd963815",
+      "eid": "ev:analysis:faa7de010775194d",
       "kind": "analysis",
       "locator": {},
       "ref": "A1",
@@ -5848,8 +14353,8 @@
       "item": "wow",
       "node_id": "card",
       "label": "异动分析",
-      "artifact": "c3ab06e1330c51f0567f410f6275a2726a38cad2bd092e145dc6c70260545e9b",
-      "eid": "ev:analysis:88367707fd963815",
+      "artifact": "a234a885f55bde88562ab83886da7a3f2c60f9cc352ce05ca6481d32166886f0",
+      "eid": "ev:analysis:faa7de010775194d",
       "eid_ok": true,
       "cited": {
        "what": "phrase"
@@ -5917,7 +14422,7 @@
       "ref": "weekly",
       "role": "data",
       "role_label": "数据",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "columns": [
        "week",
        "gmv"
@@ -5930,7 +14435,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "node_id": "weekly",
       "tool": "db_query__shop",
       "source": "shop",
@@ -5991,7 +14496,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -6011,7 +14516,7 @@
       "step": "analysis_seal",
       "alias": "A1",
       "node_id": "card",
-      "artifact": "c3ab06e1330c51f0567f410f6275a2726a38cad2bd092e145dc6c70260545e9b",
+      "artifact": "a234a885f55bde88562ab83886da7a3f2c60f9cc352ce05ca6481d32166886f0",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -6032,7 +14537,7 @@
    "s9": {
     "report": {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75"
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9"
     },
     "segment": {
      "id": "s9",
@@ -6047,7 +14552,7 @@
      "ref": "v:A2.r0.delta",
      "cite": {
       "alias": "A2",
-      "eid": "ev:cell:8f04cbf82264a3bb",
+      "eid": "ev:cell:289f04710d52804d",
       "kind": "cell",
       "locator": {
        "column": "delta",
@@ -6092,8 +14597,8 @@
       "item": "where",
       "node_id": "card",
       "label": "异动分析",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
-      "eid": "ev:analysis:b11cf44f63eaa443",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
+      "eid": "ev:analysis:f872ae8bf6eeaee1",
       "eid_ok": true,
       "cited": {
        "what": "cell",
@@ -6215,7 +14720,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
        },
        {
         "key": "segments",
@@ -6235,7 +14740,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        },
        {
         "key": "top_key",
@@ -6245,7 +14750,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        }
       ],
       "formulas": [
@@ -6291,39 +14796,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
         ],
         [
          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
         ],
         [
          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
         ],
         [
          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
         ]
        ],
        "masked": [],
@@ -6343,7 +14848,7 @@
       "ref": "weekly.r0.gmv",
       "role": "headline_base",
       "role_label": "基期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 0,
       "column": "gmv",
       "value": "2700",
@@ -6365,7 +14870,7 @@
       "ref": "weekly.r1.gmv",
       "role": "headline_target",
       "role_label": "本期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -6387,7 +14892,7 @@
       "ref": "by_region",
       "role": "data",
       "role_label": "数据",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "columns": [
        "region",
        "week",
@@ -6401,7 +14906,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "node_id": "weekly",
       "tool": "db_query__shop",
       "source": "shop",
@@ -6453,7 +14958,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -6465,7 +14970,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "node_id": "by_region",
       "tool": "db_query__shop",
       "source": "shop",
@@ -6653,7 +15158,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -6673,7 +15178,7 @@
       "step": "analysis_seal",
       "alias": "A2",
       "node_id": "card",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -6694,7 +15199,7 @@
    "s18": {
     "report": {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75"
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9"
     },
     "segment": {
      "id": "s18",
@@ -6709,7 +15214,7 @@
      "ref": "v:A2.r0.delta",
      "cite": {
       "alias": "A2",
-      "eid": "ev:cell:8f04cbf82264a3bb",
+      "eid": "ev:cell:289f04710d52804d",
       "kind": "cell",
       "locator": {
        "column": "delta",
@@ -6754,8 +15259,8 @@
       "item": "where",
       "node_id": "card",
       "label": "异动分析",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
-      "eid": "ev:analysis:b11cf44f63eaa443",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
+      "eid": "ev:analysis:f872ae8bf6eeaee1",
       "eid_ok": true,
       "cited": {
        "what": "cell",
@@ -6877,7 +15382,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
        },
        {
         "key": "segments",
@@ -6897,7 +15402,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        },
        {
         "key": "top_key",
@@ -6907,7 +15412,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        }
       ],
       "formulas": [
@@ -6953,39 +15458,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
         ],
         [
          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
         ],
         [
          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
         ],
         [
          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
         ]
        ],
        "masked": [],
@@ -7005,7 +15510,7 @@
       "ref": "weekly.r0.gmv",
       "role": "headline_base",
       "role_label": "基期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 0,
       "column": "gmv",
       "value": "2700",
@@ -7027,7 +15532,7 @@
       "ref": "weekly.r1.gmv",
       "role": "headline_target",
       "role_label": "本期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -7049,7 +15554,7 @@
       "ref": "by_region",
       "role": "data",
       "role_label": "数据",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "columns": [
        "region",
        "week",
@@ -7063,7 +15568,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "node_id": "weekly",
       "tool": "db_query__shop",
       "source": "shop",
@@ -7115,7 +15620,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -7127,7 +15632,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "node_id": "by_region",
       "tool": "db_query__shop",
       "source": "shop",
@@ -7315,7 +15820,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -7335,7 +15840,7 @@
       "step": "analysis_seal",
       "alias": "A2",
       "node_id": "card",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -7355,7 +15860,7 @@
    }
   },
   "graph": {
-   "run_id": "ffeb7bfbcde6480fbddb573a6c6558af",
+   "run_id": "803b3fd6fc66470b8d0c8353cfd87899",
    "schema": "agentlab.evidence/1",
    "seal": {
     "sealed": true,
@@ -7366,7 +15871,7 @@
    "reports": [
     {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75",
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9",
      "doc_sealed": true,
      "hash_ok": true,
      "ok": true,
@@ -7403,11 +15908,11 @@
    "evidence": [
     {
      "alias": "A1",
-     "eid": "ev:analysis:88367707fd963815",
+     "eid": "ev:analysis:faa7de010775194d",
      "kind": "analysis",
      "label": "波动基线 · 波动基线没有做成：只有 0 期历史，至少要 8 期",
      "node_id": "card",
-     "artifact": "c3ab06e1330c51f0567f410f6275a2726a38cad2bd092e145dc6c70260545e9b",
+     "artifact": "a234a885f55bde88562ab83886da7a3f2c60f9cc352ce05ca6481d32166886f0",
      "sealed": true,
      "cited_by": [
       "s6"
@@ -7422,11 +15927,11 @@
     },
     {
      "alias": "A2",
-     "eid": "ev:analysis:b11cf44f63eaa443",
+     "eid": "ev:analysis:f872ae8bf6eeaee1",
      "kind": "analysis",
      "label": "变化拆解（可加） · 销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
      "node_id": "card",
-     "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+     "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
      "sealed": true,
      "cited_by": [
       "s3",
@@ -7447,11 +15952,11 @@
     },
     {
      "alias": "Q1",
-     "eid": "ev:query:30fcabb3fa33690b",
+     "eid": "ev:query:1697ecefc4f957ae",
      "kind": "query",
      "label": "db_query__shop · 2 行",
      "node_id": "weekly",
-     "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+     "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -7467,11 +15972,11 @@
     },
     {
      "alias": "Q2",
-     "eid": "ev:query:d3b62a2e9c2b52a7",
+     "eid": "ev:query:9edc88bec38d5ca5",
      "kind": "query",
      "label": "db_query__shop · 8 行",
      "node_id": "by_region",
-     "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+     "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -7546,7 +16051,7 @@
    "mode": "cited"
   },
   "audit": {
-   "run_id": "ffeb7bfbcde6480fbddb573a6c6558af",
+   "run_id": "803b3fd6fc66470b8d0c8353cfd87899",
    "schema": "agentlab.evidence.audit/1",
    "mode": "cited",
    "seal": {
@@ -7560,7 +16065,7 @@
    "reports": [
     {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75",
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9",
      "hash_ok": true,
      "doc_sealed": true,
      "fields": [
@@ -7612,8 +16117,8 @@
        "alias": "A2",
        "evidence_kind": "analysis",
        "evidence": "变化拆解（可加） · 销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
-       "eid": "ev:analysis:b11cf44f63eaa443",
-       "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+       "eid": "ev:analysis:f872ae8bf6eeaee1",
+       "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
        "node_id": "card",
        "sealed": true,
        "span": [
@@ -7637,8 +16142,8 @@
        "alias": "A1",
        "evidence_kind": "analysis",
        "evidence": "波动基线 · 波动基线没有做成：只有 0 期历史，至少要 8 期",
-       "eid": "ev:analysis:88367707fd963815",
-       "artifact": "c3ab06e1330c51f0567f410f6275a2726a38cad2bd092e145dc6c70260545e9b",
+       "eid": "ev:analysis:faa7de010775194d",
+       "artifact": "a234a885f55bde88562ab83886da7a3f2c60f9cc352ce05ca6481d32166886f0",
        "node_id": "card",
        "sealed": true,
        "span": [
@@ -7662,8 +16167,8 @@
        "alias": "A2",
        "evidence_kind": "analysis",
        "evidence": "A2.r0.delta",
-       "eid": "ev:cell:8f04cbf82264a3bb",
-       "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+       "eid": "ev:cell:289f04710d52804d",
+       "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
        "node_id": "card",
        "sealed": true,
        "span": [
@@ -7687,8 +16192,8 @@
        "alias": "A2",
        "evidence_kind": "analysis",
        "evidence": "A2.r0.delta",
-       "eid": "ev:cell:8f04cbf82264a3bb",
-       "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+       "eid": "ev:cell:289f04710d52804d",
+       "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
        "node_id": "card",
        "sealed": true,
        "span": [
@@ -7729,10 +16234,10 @@
   },
   "analyses": {
    "A1": {
-    "run_id": "ffeb7bfbcde6480fbddb573a6c6558af",
+    "run_id": "803b3fd6fc66470b8d0c8353cfd87899",
     "report": {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75"
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9"
     },
     "alias": "A1",
     "rendered": "波动基线没有做成：只有 0 期历史，至少要 8 期",
@@ -7747,8 +16252,8 @@
       "item": "wow",
       "node_id": "card",
       "label": "异动分析",
-      "artifact": "c3ab06e1330c51f0567f410f6275a2726a38cad2bd092e145dc6c70260545e9b",
-      "eid": "ev:analysis:88367707fd963815",
+      "artifact": "a234a885f55bde88562ab83886da7a3f2c60f9cc352ce05ca6481d32166886f0",
+      "eid": "ev:analysis:faa7de010775194d",
       "eid_ok": true,
       "cited": {
        "what": "phrase"
@@ -7816,7 +16321,7 @@
       "ref": "weekly",
       "role": "data",
       "role_label": "数据",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "columns": [
        "week",
        "gmv"
@@ -7829,7 +16334,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "node_id": "weekly",
       "tool": "db_query__shop",
       "source": "shop",
@@ -7890,7 +16395,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -7910,7 +16415,7 @@
       "step": "analysis_seal",
       "alias": "A1",
       "node_id": "card",
-      "artifact": "c3ab06e1330c51f0567f410f6275a2726a38cad2bd092e145dc6c70260545e9b",
+      "artifact": "a234a885f55bde88562ab83886da7a3f2c60f9cc352ce05ca6481d32166886f0",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -7927,10 +16432,10 @@
     }
    },
    "A2": {
-    "run_id": "ffeb7bfbcde6480fbddb573a6c6558af",
+    "run_id": "803b3fd6fc66470b8d0c8353cfd87899",
     "report": {
      "node_id": "write",
-     "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75"
+     "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9"
     },
     "alias": "A2",
     "rendered": "销售额下降 340（-12.6%），主要来自华东（-300，占 88.2%）",
@@ -7945,8 +16450,8 @@
       "item": "where",
       "node_id": "card",
       "label": "异动分析",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
-      "eid": "ev:analysis:b11cf44f63eaa443",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
+      "eid": "ev:analysis:f872ae8bf6eeaee1",
       "eid_ok": true,
       "cited": {
        "what": "phrase"
@@ -8064,7 +16569,7 @@
        {
         "key": "pct_total",
         "raw": "-0.125925925926",
-        "rendered": "-0.1259259259"
+        "rendered": "-12.6%"
        },
        {
         "key": "segments",
@@ -8084,7 +16589,7 @@
        {
         "key": "top_k_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        },
        {
         "key": "top_key",
@@ -8094,7 +16599,7 @@
        {
         "key": "top_share",
         "raw": "0.882352941176",
-        "rendered": "0.8823529412"
+        "rendered": "88.2%"
        }
       ],
       "formulas": [
@@ -8140,39 +16645,39 @@
        "rows": [
         [
          "华东",
-         "1000",
+         "1,000",
          "700",
          "-300",
-         "-0.3",
-         "0.882352941176",
-         null
+         "-30.0%",
+         "88.2%",
+         "—"
         ],
         [
          "华南",
          "800",
          "780",
          "-20",
-         "-0.025",
-         "0.0588235294118",
-         null
+         "-2.5%",
+         "5.9%",
+         "—"
         ],
         [
          "华北",
          "600",
          "590",
          "-10",
-         "-0.0166666666667",
-         "0.0294117647059",
-         null
+         "-1.7%",
+         "2.9%",
+         "—"
         ],
         [
          "西南",
          "300",
          "290",
          "-10",
-         "-0.0333333333333",
-         "0.0294117647059",
-         null
+         "-3.3%",
+         "2.9%",
+         "—"
         ]
        ],
        "masked": [],
@@ -8187,7 +16692,7 @@
       "ref": "weekly.r0.gmv",
       "role": "headline_base",
       "role_label": "基期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 0,
       "column": "gmv",
       "value": "2700",
@@ -8209,7 +16714,7 @@
       "ref": "weekly.r1.gmv",
       "role": "headline_target",
       "role_label": "本期总数",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "row": 1,
       "column": "gmv",
       "value": "2360",
@@ -8231,7 +16736,7 @@
       "ref": "by_region",
       "role": "data",
       "role_label": "数据",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "columns": [
        "region",
        "week",
@@ -8245,7 +16750,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "ec8a1a81d76fc960f4028b7e93e60e32cc84be7249edf18ef550970a6c59611e",
+      "artifact": "d22f4912d7727581f89f306343bbe2fa8392d35f001de792afd9612d0c3a1c76",
       "node_id": "weekly",
       "tool": "db_query__shop",
       "source": "shop",
@@ -8297,7 +16802,7 @@
        "gmv": "number",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -8309,7 +16814,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "81b47f64b0d0919842039e3492a594d69dea9a25e7f6b93ff0efc3d5bebbd0a9",
+      "artifact": "36ed6a1a4443227c0d747c5621516f59ce7a5cc345dec576c97e7f4c6ab6ebce",
       "node_id": "by_region",
       "tool": "db_query__shop",
       "source": "shop",
@@ -8497,7 +17002,7 @@
        "region": "text",
        "week": "text"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -8517,7 +17022,7 @@
       "step": "analysis_seal",
       "alias": "A2",
       "node_id": "card",
-      "artifact": "fce97813bea8e5e060681d5d44249705e86a6feb1f5912165984aec0c951f7dc",
+      "artifact": "6bb7505a929ec15dce8676bcb6765c621a5d15c56ae66fa71b6da52183c343ac",
       "ledger": true,
       "hash_ok": true,
       "eid_ok": true,
@@ -8539,7 +17044,7 @@
    "tier": "formal",
    "report": {
     "node_id": "write",
-    "doc_artifact": "047519f5bcdfea3056d2a226585cb7c33969a9f6b908940672b6bfee16a8ec75"
+    "doc_artifact": "0dfdadc2c8c470f7840b80525ae902ccbe0787e5d362fdd5a3fd2b5e8e79edb9"
    },
    "calibers": [],
    "metrics_checked": 0,
@@ -8561,7 +17066,7 @@
       84,
       88
      ],
-     "eid": "ev:cell:8f04cbf82264a3bb"
+     "eid": "ev:cell:289f04710d52804d"
     },
     {
      "token": "-300",
@@ -8575,7 +17080,7 @@
       120,
       124
      ],
-     "eid": "ev:cell:8f04cbf82264a3bb"
+     "eid": "ev:cell:289f04710d52804d"
     }
    ],
    "stats": {
@@ -8599,13 +17104,13 @@
     "analysis_claims": 0
    },
    "gaps": [],
-   "declared_at": "2026-10-21T10:36:36.611189+00:00"
+   "declared_at": "2026-10-21T15:22:25.297775+00:00"
   }
  },
  "metric": {
-  "run_id": "153afe100eb0406e8be4d9dc7adac8d6",
+  "run_id": "9367b703c01d492891d1ae80113eb973",
   "run_class": "exploratory",
-  "doc_artifact": "e8572d3a890d844783f980891af048db3b71d7533b85ce3ca00926acc199fdca",
+  "doc_artifact": "e75fd02f25fef1593d17c4151f632755cdda3bcbfe7060c43ef4206fbcc218aa",
   "doc": {
    "analysis_claims": {
     "lexicon": 1,
@@ -8637,7 +17142,7 @@
         {
          "cite": {
           "alias": "m:wow",
-          "eid": "ev:metric:a2ec409041e4deb7",
+          "eid": "ev:metric:f800d62ed5db82c6",
           "kind": "metric",
           "locator": {
            "metric": "wow"
@@ -8680,11 +17185,11 @@
    "catalog": {
     "Q1": {
      "alias": "Q1",
-     "artifact": "5659261ab82f038f44106c558cab3c57aed2e86eb9eb0426d34c81cfc5029278",
+     "artifact": "7f6252ec2ffe6a8818000b03eba0804096439a0ea0f0a90d783d11efc2a97895",
      "columns": [
       "gmv"
      ],
-     "eid": "ev:query:60498acf9bf713c7",
+     "eid": "ev:query:6f585173ecdd78d9",
      "exec": 1,
      "kind": "query",
      "label": "db_query__shop · 1 行",
@@ -8694,15 +17199,15 @@
      "source": "shop",
      "tool": "db_query__shop",
      "truncated": false,
-     "via": "03718830cc3618e9dd3bc6bb750cc06845e4e2cc35b90499c02826e79f8441c1"
+     "via": "87eb650424e1cc8ac89d2fbeb4b9125a5c18c9572d80d40ce905975bc8a20a5b"
     },
     "Q2": {
      "alias": "Q2",
-     "artifact": "0f9d43f91a5ea754fca6cf494fc69b373fa1fadd85eaccf1dbca2b1d65542e2c",
+     "artifact": "843e7e96c4a5c036093203b54af6d7f11565aa9bb38db53b2df49c04ac2844ec",
      "columns": [
       "gmv"
      ],
-     "eid": "ev:query:62b02f6bb6886f1c",
+     "eid": "ev:query:25033729f2cf3072",
      "exec": 1,
      "kind": "query",
      "label": "db_query__shop · 1 行",
@@ -8712,20 +17217,20 @@
      "source": "shop",
      "tool": "db_query__shop",
      "truncated": false,
-     "via": "e42968afbe8ea4ca5b056fb9535eda96efba5113e3feedcff683a8c84f4dbccb"
+     "via": "0e72565a4da0b50a98b25cb5541aaab205ccd3e079e782598490c2615f4321ee"
     },
     "m:wow": {
      "alias": "m:wow",
-     "artifact": "bc5a8e29e70745eed2c7dfabc866532ba1ef1953cbc1cc8d77d9eb02da0cdf84",
+     "artifact": "ee4f584270883dad6b80a231a42f95212d4d166f5b61f6398973a3a16cbea115",
      "caliber": "周报口径",
      "comparability": {
-      "artifact": "f5d88bbccf1fdaf6ad3724cf6a2f5e0eea5defdd386e8c85cf663bc2ec14fde2",
+      "artifact": "95fae782d86479a4acd9888355ac5b8a47378a7fe2f49037c7a4416c93f2123c",
       "phrase": "两期可比，窗口长度、整期、星期构成核对不了",
       "reasons": [],
       "verdict": "comparable"
      },
      "decimals": 3,
-     "eid": "ev:metric:a2ec409041e4deb7",
+     "eid": "ev:metric:f800d62ed5db82c6",
      "format": "percent_of_ratio",
      "kind": "metric",
      "label": "环比 = -12.6%",
@@ -8745,7 +17250,7 @@
    "markdown": "环比 -12.6%。",
    "node_id": "write",
    "provenance": 1,
-   "run_id": "153afe100eb0406e8be4d9dc7adac8d6",
+   "run_id": "9367b703c01d492891d1ae80113eb973",
    "schema": "agentlab.report/1",
    "source": "环比 [[m:wow]]。",
    "stats": {
@@ -8774,7 +17279,7 @@
    "s1": {
     "report": {
      "node_id": "write",
-     "doc_artifact": "e8572d3a890d844783f980891af048db3b71d7533b85ce3ca00926acc199fdca"
+     "doc_artifact": "e75fd02f25fef1593d17c4151f632755cdda3bcbfe7060c43ef4206fbcc218aa"
     },
     "segment": {
      "id": "s1",
@@ -8789,7 +17294,7 @@
      "ref": "m:wow",
      "cite": {
       "alias": "m:wow",
-      "eid": "ev:metric:a2ec409041e4deb7",
+      "eid": "ev:metric:f800d62ed5db82c6",
       "kind": "metric",
       "locator": {
        "metric": "wow"
@@ -8841,8 +17346,8 @@
       "recompute_ok": true,
       "status": "ok",
       "node_id": "kpi",
-      "artifact": "bc5a8e29e70745eed2c7dfabc866532ba1ef1953cbc1cc8d77d9eb02da0cdf84",
-      "eid": "ev:metric:a2ec409041e4deb7",
+      "artifact": "ee4f584270883dad6b80a231a42f95212d4d166f5b61f6398973a3a16cbea115",
+      "eid": "ev:metric:f800d62ed5db82c6",
       "eid_ok": true,
       "hash_ok": true,
       "render_ok": true,
@@ -8853,7 +17358,7 @@
        "verdict": "comparable",
        "phrase": "两期可比，窗口长度、整期、星期构成核对不了",
        "reasons": [],
-       "artifact": "f5d88bbccf1fdaf6ad3724cf6a2f5e0eea5defdd386e8c85cf663bc2ec14fde2",
+       "artifact": "95fae782d86479a4acd9888355ac5b8a47378a7fe2f49037c7a4416c93f2123c",
        "hash_ok": true,
        "sealed": true,
        "method": "comparable@1",
@@ -8870,8 +17375,8 @@
          "origin": "metric",
          "node_id": "kpi",
          "label": "周报口径",
-         "artifact": "f5d88bbccf1fdaf6ad3724cf6a2f5e0eea5defdd386e8c85cf663bc2ec14fde2",
-         "eid": "ev:analysis:3cfd497192a30e70",
+         "artifact": "95fae782d86479a4acd9888355ac5b8a47378a7fe2f49037c7a4416c93f2123c",
+         "eid": "ev:analysis:fbedd7c089d2595c",
          "eid_ok": true,
          "cited": {
           "what": "phrase"
@@ -8968,37 +17473,37 @@
            [
             "同口径",
             "通过",
-            null,
-            null,
-            null
+            "—",
+            "—",
+            "—"
            ],
            [
             "窗口长度",
             "核对不了",
-            null,
-            null,
-            null
+            "—",
+            "—",
+            "—"
            ],
            [
             "整期",
             "核对不了",
             "核对不了",
             "核对不了",
-            null
+            "—"
            ],
            [
             "星期构成",
             "核对不了",
-            null,
-            null,
-            null
+            "—",
+            "—",
+            "—"
            ],
            [
             "数据问题",
             "通过",
-            null,
-            null,
-            null
+            "—",
+            "—",
+            "—"
            ]
           ],
           "masked": [],
@@ -9013,7 +17518,7 @@
          "ref": "last_week",
          "role": "base",
          "role_label": "基期",
-         "artifact": "0f9d43f91a5ea754fca6cf494fc69b373fa1fadd85eaccf1dbca2b1d65542e2c",
+         "artifact": "843e7e96c4a5c036093203b54af6d7f11565aa9bb38db53b2df49c04ac2844ec",
          "columns": [
           "gmv"
          ],
@@ -9028,7 +17533,7 @@
          "ref": "this_week",
          "role": "target",
          "role_label": "本期",
-         "artifact": "5659261ab82f038f44106c558cab3c57aed2e86eb9eb0426d34c81cfc5029278",
+         "artifact": "7f6252ec2ffe6a8818000b03eba0804096439a0ea0f0a90d783d11efc2a97895",
          "columns": [
           "gmv"
          ],
@@ -9048,7 +17553,7 @@
          "step": "analysis_seal",
          "alias": "",
          "node_id": "kpi",
-         "artifact": "f5d88bbccf1fdaf6ad3724cf6a2f5e0eea5defdd386e8c85cf663bc2ec14fde2",
+         "artifact": "95fae782d86479a4acd9888355ac5b8a47378a7fe2f49037c7a4416c93f2123c",
          "ledger": false,
          "hash_ok": true,
          "eid_ok": true,
@@ -9062,8 +17567,8 @@
      },
      {
       "step": "input",
-      "artifact": "5659261ab82f038f44106c558cab3c57aed2e86eb9eb0426d34c81cfc5029278",
-      "eid": "ev:cell:31abb0e1cb0cb3ca",
+      "artifact": "7f6252ec2ffe6a8818000b03eba0804096439a0ea0f0a90d783d11efc2a97895",
+      "eid": "ev:cell:b2aca6553acd6f1f",
       "locator": {
        "column": "gmv",
        "row": 0
@@ -9078,8 +17583,8 @@
      },
      {
       "step": "input",
-      "artifact": "0f9d43f91a5ea754fca6cf494fc69b373fa1fadd85eaccf1dbca2b1d65542e2c",
-      "eid": "ev:cell:f3221a76e3c58c31",
+      "artifact": "843e7e96c4a5c036093203b54af6d7f11565aa9bb38db53b2df49c04ac2844ec",
+      "eid": "ev:cell:85ff4ca6a21650c7",
       "locator": {
        "column": "gmv",
        "row": 0
@@ -9095,7 +17600,7 @@
      {
       "step": "query",
       "alias": "Q1",
-      "artifact": "5659261ab82f038f44106c558cab3c57aed2e86eb9eb0426d34c81cfc5029278",
+      "artifact": "7f6252ec2ffe6a8818000b03eba0804096439a0ea0f0a90d783d11efc2a97895",
       "node_id": "this_week",
       "tool": "db_query__shop",
       "source": "shop",
@@ -9134,7 +17639,7 @@
       "column_types": {
        "gmv": "number"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -9145,7 +17650,7 @@
      {
       "step": "query",
       "alias": "Q2",
-      "artifact": "0f9d43f91a5ea754fca6cf494fc69b373fa1fadd85eaccf1dbca2b1d65542e2c",
+      "artifact": "843e7e96c4a5c036093203b54af6d7f11565aa9bb38db53b2df49c04ac2844ec",
       "node_id": "last_week",
       "tool": "db_query__shop",
       "source": "shop",
@@ -9184,7 +17689,7 @@
       "column_types": {
        "gmv": "number"
       },
-      "schema_artifact": "4c3229daead9cdde26fab4b3156cfc17eba8cec291a35b54e2af392a8a694aec",
+      "schema_artifact": "3324dd9c82f1d9ec2771f14ffbcf2344e459b9864e6a7e3106e516590f263f65",
       "catalog": [
        {
         "table": "orders",
@@ -9206,7 +17711,7 @@
    }
   },
   "graph": {
-   "run_id": "153afe100eb0406e8be4d9dc7adac8d6",
+   "run_id": "9367b703c01d492891d1ae80113eb973",
    "schema": "agentlab.evidence/1",
    "seal": {
     "sealed": true,
@@ -9217,7 +17722,7 @@
    "reports": [
     {
      "node_id": "write",
-     "doc_artifact": "e8572d3a890d844783f980891af048db3b71d7533b85ce3ca00926acc199fdca",
+     "doc_artifact": "e75fd02f25fef1593d17c4151f632755cdda3bcbfe7060c43ef4206fbcc218aa",
      "doc_sealed": true,
      "hash_ok": true,
      "ok": true,
@@ -9254,11 +17759,11 @@
    "evidence": [
     {
      "alias": "Q1",
-     "eid": "ev:query:60498acf9bf713c7",
+     "eid": "ev:query:6f585173ecdd78d9",
      "kind": "query",
      "label": "db_query__shop · 1 行",
      "node_id": "this_week",
-     "artifact": "5659261ab82f038f44106c558cab3c57aed2e86eb9eb0426d34c81cfc5029278",
+     "artifact": "7f6252ec2ffe6a8818000b03eba0804096439a0ea0f0a90d783d11efc2a97895",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -9273,11 +17778,11 @@
     },
     {
      "alias": "Q2",
-     "eid": "ev:query:62b02f6bb6886f1c",
+     "eid": "ev:query:25033729f2cf3072",
      "kind": "query",
      "label": "db_query__shop · 1 行",
      "node_id": "last_week",
-     "artifact": "0f9d43f91a5ea754fca6cf494fc69b373fa1fadd85eaccf1dbca2b1d65542e2c",
+     "artifact": "843e7e96c4a5c036093203b54af6d7f11565aa9bb38db53b2df49c04ac2844ec",
      "sealed": true,
      "cited_by": [],
      "units": [],
@@ -9292,11 +17797,11 @@
     },
     {
      "alias": "m:wow",
-     "eid": "ev:metric:a2ec409041e4deb7",
+     "eid": "ev:metric:f800d62ed5db82c6",
      "kind": "metric",
      "label": "环比 = -12.6%",
      "node_id": "kpi",
-     "artifact": "bc5a8e29e70745eed2c7dfabc866532ba1ef1953cbc1cc8d77d9eb02da0cdf84",
+     "artifact": "ee4f584270883dad6b80a231a42f95212d4d166f5b61f6398973a3a16cbea115",
      "sealed": true,
      "cited_by": [
       "s1"
@@ -9346,7 +17851,7 @@
    "mode": "cited"
   },
   "audit": {
-   "run_id": "153afe100eb0406e8be4d9dc7adac8d6",
+   "run_id": "9367b703c01d492891d1ae80113eb973",
    "schema": "agentlab.evidence.audit/1",
    "mode": "cited",
    "seal": {
@@ -9360,7 +17865,7 @@
    "reports": [
     {
      "node_id": "write",
-     "doc_artifact": "e8572d3a890d844783f980891af048db3b71d7533b85ce3ca00926acc199fdca",
+     "doc_artifact": "e75fd02f25fef1593d17c4151f632755cdda3bcbfe7060c43ef4206fbcc218aa",
      "hash_ok": true,
      "doc_sealed": true,
      "fields": [
@@ -9412,8 +17917,8 @@
        "alias": "m:wow",
        "evidence_kind": "metric",
        "evidence": "环比 = -12.6%",
-       "eid": "ev:metric:a2ec409041e4deb7",
-       "artifact": "bc5a8e29e70745eed2c7dfabc866532ba1ef1953cbc1cc8d77d9eb02da0cdf84",
+       "eid": "ev:metric:f800d62ed5db82c6",
+       "artifact": "ee4f584270883dad6b80a231a42f95212d4d166f5b61f6398973a3a16cbea115",
        "node_id": "kpi",
        "sealed": true,
        "span": [
@@ -9458,7 +17963,7 @@
    "tier": "formal",
    "report": {
     "node_id": "write",
-    "doc_artifact": "e8572d3a890d844783f980891af048db3b71d7533b85ce3ca00926acc199fdca"
+    "doc_artifact": "e75fd02f25fef1593d17c4151f632755cdda3bcbfe7060c43ef4206fbcc218aa"
    },
    "calibers": [],
    "metrics_checked": 0,
@@ -9480,7 +17985,7 @@
       3,
       9
      ],
-     "eid": "ev:metric:a2ec409041e4deb7"
+     "eid": "ev:metric:f800d62ed5db82c6"
     }
    ],
    "stats": {
@@ -9504,7 +18009,7 @@
     "analysis_claims": 0
    },
    "gaps": [],
-   "declared_at": "2026-10-21T10:36:37.326911+00:00"
+   "declared_at": "2026-10-21T15:22:25.849170+00:00"
   }
  }
 }
diff --git a/scripts/check-evidence.mjs b/scripts/check-evidence.mjs
index 738cd501..e3699549 100644
--- a/scripts/check-evidence.mjs
+++ b/scripts/check-evidence.mjs
@@ -614,6 +614,15 @@ await section('analysis', '分析结论：判定语、具名的数、结果表
   check('计算一步：关键数的代入式照服务端', (await calc.locator('[data-ev-analysis-formulas]').innerText()).includes('华东的占比 = 华东的变化 ÷ 总变化 = -300 ÷ （-340） = 88.2%'))
   check('……具名的数、结果表（表头是给人看的列名）', await calc.locator('[data-ev-analysis-value="top_share"]').count() === 1
     && (await calc.locator('[data-ev-analysis-table] th').allInnerTexts()).join('、').includes('占变化的比重'))
+  // 第 2 波 S1：占比、变化率按百分数显示（以前照原样写 0.8823529412），空格写「—」不写 null
+  check('……具名的数按结论记的格式写：占比 88.2%，不是 0.8823529412',
+    (await calc.locator('[data-ev-analysis-value="top_share"]').innerText()).includes('88.2%')
+    && !(await calc.locator('[data-ev-analysis-value="top_share"]').innerText()).includes('0.88'))
+  const calcRows = (await calc.locator('[data-ev-analysis-table] tbody tr').allInnerTexts()).map((t) => t.replace(/\s+/g, ' '))
+  check('……结果表照结论记的格式写：变化率 -30.0%、占变化的比重 88.2%，空格写「—」',
+    calcRows[0].includes('-30.0%') && calcRows[0].includes('88.2%') && !calcRows[0].includes('null')
+    && !calcRows[0].includes('-0.3') && !(await calc.locator('[data-ev-analysis-table] tbody').innerText()).includes('null'),
+    calcRows[0])
   const inputs = p.locator('[data-ev-analysis-input]')
   check('输入一步：每份一节（基期总数、本期总数、数据）', JSON.stringify(await inputs.evaluateAll((els) => els.map((e) => e.dataset.evAnalysisInput)))
     === JSON.stringify(['headline_base', 'headline_target', 'data']))
@@ -630,6 +639,14 @@ await section('analysis', '分析结论：判定语、具名的数、结果表
   check('分析结论的数不走口径账本：没有「口径」一步', await p.locator('[data-ev-ledger]').count() === 0)
   check('面板底下写服务端的说明（判定只说明变化来自哪里）', (await p.locator('[data-ev-analysis-note]').innerText()).includes('判定只说明变化来自哪里'))
 
+  // 没写换算的具名的数、结果表的格：按结论记的格式显示（第 2 波 S1），正文、出处链、整表一致
+  for (const [sid, text] of [['s19', '-30.0%'], ['s21', '88.2%'], ['s23', '88.2%']]) {
+    check(`没写换算的 ${sid === 's23' ? '具名的数 [[a:A1.top_share]]' : '结果表的格 [[v:A1.r0.' + (sid === 's19' ? 'pct' : 'share') + ']]'}按结论记的格式显示 ${text}`,
+      (await box.locator(`[data-seg="${sid}"]`).innerText()) === text, await box.locator(`[data-seg="${sid}"]`).innerText())
+  }
+  check('……整表 [[table:A1 cols=region,delta,pct,share]] 的格也按格式写', (await box.locator('[data-seg="s38"]').innerText()) === '-30.0%'
+    && (await box.locator('[data-seg="s40"]').innerText()) === '88.2%')
+
   // 具名的数、结果表的格：同一条链，「这一处引用的是」写清是哪一项，被引用的那个描出来
   await box.locator('[data-seg="s9"]').click()
   await p.locator('[data-ev-analysis-cited="value"]').waitFor({ timeout: 8000 })
diff --git a/scripts/check-runs.mjs b/scripts/check-runs.mjs
index 904f1c37..1809305a 100644
--- a/scripts/check-runs.mjs
+++ b/scripts/check-runs.mjs
@@ -2270,7 +2270,11 @@ await section('证据页签：分析说法违规的审计行写种类的叫法
       && claimed.every((r) => r.label.includes('，') && !r.label.includes('无证据')), JSON.stringify(claimed.map((r) => r.label)))
     check('……别的无证据行照旧写「无证据」（没挂依据的两句结论）', rows.filter((r) => !r.issue && r.state === '?无证据').length === 2,
       JSON.stringify(rows.map((r) => `${r.issue}:${r.state}`)))
-    check('……有出处那一组：判定语、具名的数、结果表的格三行', rows.filter((r) => r.state.includes('有出处')).length === 3, JSON.stringify(rows.map((r) => r.state)))
+    // 第 2 波 S1 起报告多引了变化率、比重两格、一处没写换算的具名的数、整表展开的 8 格：有出处那一组的行数照夹具里
+    // 审计的「有出处」一组数（不再写死 3）
+    const cited = a.audit.groups.find((g) => g.key === 'cited')?.count ?? 0
+    check('……有出处那一组：判定语、具名的数、结果表的各格，行数和审计的一致', cited > 3
+      && rows.filter((r) => r.state.includes('有出处')).length === cited, `${cited} / ${JSON.stringify(rows.map((r) => r.state))}`)
     await page.route(new RegExp(`/api/runs/${EV_A}/evidence/segments/`), (r) => {
       const sid = decodeURIComponent(new URL(r.request().url()).pathname.split('/').pop())
       return a.segments[sid] ? r.fulfill({ json: a.segments[sid] }) : r.fulfill({ status: 404, json: { detail: '没有这个片段' } })