From bcf0c86feb14427bb7c06709c962ceec2c7e7693 Mon Sep 17 00:00:00 2001
From: Jesse_Chen <chen.yue@mevixaga.com>
Date: Tue, 29 Sep 2026 08:56:03 +0800
Subject: [PATCH] =?UTF-8?q?research(rectification):=20typed=20events=20sco?=
 =?UTF-8?q?red=20as=20answered=20probes=20=E2=80=94=20no=20benefit=20(BUG-?=
 =?UTF-8?q?1089)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

v4 open holdout, ±10/±30/±60, raw and percent priors: only 4/9/14 of 57
day-precision training events split candidates; widths unchanged, top-1
drops. Narrowed year blocking (R3) mixed. All arms no_benefit; no
implementation brief recommended. Two runs byte-identical.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017eEAG8HD3mm8gsKXgk8uU8
---
 docs/BUG_HISTORY.md                           |   18 +-
 ...cation_typed_event_scoring_2026_09_29.json | 8205 +++++++++++++++++
 ...fication_typed_event_scoring_2026_09_29.md |  104 +
 ...ification-typed-event-research-20260929.md |   51 +
 docs/tasks/README.md                          |    2 +-
 scripts/research/typed_event_as_probe.py      |  391 +
 6 files changed, 8763 insertions(+), 8 deletions(-)
 create mode 100644 docs/research/rectification_typed_event_scoring_2026_09_29.json
 create mode 100644 docs/research/rectification_typed_event_scoring_2026_09_29.md
 create mode 100644 docs/tasks/PROGRESS-rectification-typed-event-research-20260929.md
 create mode 100644 scripts/research/typed_event_as_probe.py

diff --git a/docs/BUG_HISTORY.md b/docs/BUG_HISTORY.md
index 60adc3d7..358e9ad6 100644
--- a/docs/BUG_HISTORY.md
+++ b/docs/BUG_HISTORY.md
@@ -14647,25 +14647,29 @@
 
 ## BUG-1088 | 学业质量题一律问「那次上大学」，只记得年份的经历显示成「YYYY 年 1 月」
 
-- 状态：investigating
+- 状态：resolved（代码 + 回归测试 + 重新冻结；未部署，真机待合入 staging 后复核）
 - 首次发现 / 最近更新：2026-09-29 / 2026-09-29
-- 影响面：`scripts/rectification/event_probes.py::_quality_user_meaning`、`_display_date_label` / `_event_month`（冻结计分文件，ERR-110）。
+- 影响面：`scripts/rectification/event_probes.py::_quality_user_meaning`、`_display_date_label`（冻结计分文件，ERR-110）。
 - 用户现象：用户说的是某年一次艺考，卡片问「YYYY 年 1 月那次上大学，更接近如愿、将就调剂…」。
-- 根因：学业模板写死「上大学」，不看 `event_kind`；年精度经历 `date_start = YYYY-01-01`，取月不看 `precision`。
-- 修复：待执行（研究单 R0，改文字后按 ERR-110 用新 freeze 路径重新冻结）。
-- 相关记录：ERR-110
+- 触发条件：学业经历类型为 education_start / change / interruption，且出现质量题；年精度经历存为 `YYYY-01-01`。
+- 根因：学业模板写死「上大学」，不看 `event_kind`；`_display_date_label` 用 `_event_month` 取月，不看 `precision`。
+- 修复：学业分支按类型称呼（升学 / 学业变动 / 学业中断，兜底「学业经历」）；`precision == "year"` 的标签只写「YYYY 年」。`_event_month` 与 split hash 不变，计分身份不变。
+- 验证：`tests/test_rectification_event_probes.py::QualityWordingTests`（4 条）；同机 A/B（`scripts/research/quality_wording_ab.py`，`PYTHONHASHSEED=0`）只有 `user_meaning` / `display_date_label` 文本变化，分数、候选决策、题目 id / split / outcomes 逐字节相同；按 ERR-110 新建冻结 `docs/research/sealed_holdout_rerun_quality_wording_2026_09_29.freeze.json`、`docs/research/reported_offset_quality_wording_2026_09_29.freeze.json`，重放 20 / 900 次结果与 09-21 完全一致；验证完整性门禁 83 passed。
+- 防复发：年精度经历不得在任何用户文案里补月份；新增学业经历类型时同步 `EDUCATION_QUALITY_EVENT_NAME`。质量题四个选项仍含「录取」字样，未改。
+- 相关记录：ERR-110、BUG-1089
 - 复发自：无
-- 修复版本：未修
+- 修复版本：分支 `codex/rectification-typed-event-research-20260929`，未合入
 
 ## BUG-1089 | 带年月的打字经历不按选择题规则计分，还挡掉同领域相邻年份的选择题
 
-- 状态：investigating（离线研究单待执行；不上线）
+- 状态：investigating（离线研究已完成，结论不改代码；等止血单 BUG-1084 落地后由 Claude 决定是否关闭）
 - 首次发现 / 最近更新：2026-09-29 / 2026-09-29
 - 影响面：`core/build-state.ts::buildInferenceState`（evidence「是」跳过）、`event_probes.py::_existence_blocked_years`。
 - 用户现象：同一件「某年某月工作变动」，点选卡答「是」动 ±2 分，自己打字说约等于 0；说了之后那一年附近的选择题也不再出。
 - 已确认事实（虚构 12 例）：12 件经历里去掉 4 件记得到月的，选择题 5/12 例变多，最终宽度中位 17 → 13 分钟。
 - 根因：计分通道不对称，见 `docs/tasks/TASK-rectification-typed-event-scoring-research-20260929.md`。
 - 修复：未修。研究单量「打字经历按选择题边界规则计分」在 v4 开放集三档是否过门；过门才立实现单，合入前产品确认。
+- 研究结果（2026-09-29，`docs/research/rectification_typed_event_scoring_2026_09_29.md`，v4 开放集 20 例，开放集回放不是准确率）：按选择题同一判定函数给打字经历计分（R1），57 件日精度训练经历在 ±10 / ±30 / ±60 下只有 4 / 9 / 14 件能把候选分到两边，宽度中位三档不变，头名在 raw 口径下三档都降，判 `no_benefit`；挡题缩到同领域同年（R3）宽窗头名 +0.10～0.15 但 ±10 头名 −0.15，判 `no_benefit`；年月记错 ±1–3 月挤出真值 0–2.9%。结论：计分通道不对称在代码上属实，但打字经历本身多半分不开候选，改计分不划算；不立实现单。
 - 防复发：不得在研究未过门时改先验尺度或淘汰线（BUG-560）。
 - 相关记录：BUG-560、BUG-1048、BUG-1084
 - 复发自：无
diff --git a/docs/research/rectification_typed_event_scoring_2026_09_29.json b/docs/research/rectification_typed_event_scoring_2026_09_29.json
new file mode 100644
index 00000000..f731217d
--- /dev/null
+++ b/docs/research/rectification_typed_event_scoring_2026_09_29.json
@@ -0,0 +1,8205 @@
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+     "trials": 140,
+     "truth_out_rate": 0.0286
+    },
+    "R3": {
+     "mean_probe_supply": 7.1,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0,
+     "median_width": 31.0,
+     "n": 20,
+     "tie_rate": 0.45,
+     "top1": 0.5,
+     "truth_in_range": 1.0
+    },
+    "R3+R1d": {
+     "mean_probe_supply": 7.1,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.45,
+     "median_width": 30.0,
+     "n": 20,
+     "tie_rate": 0.45,
+     "top1": 0.5,
+     "truth_in_range": 1.0
+    }
+   },
+   "60": {
+    "B0": {
+     "mean_probe_supply": 7.2,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0,
+     "median_width": 53.0,
+     "n": 20,
+     "tie_rate": 0.75,
+     "top1": 0.15,
+     "truth_in_range": 1.0
+    },
+    "R1d": {
+     "mean_probe_supply": 7.2,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.7,
+     "median_width": 53.0,
+     "n": 20,
+     "tie_rate": 0.75,
+     "top1": 0.2,
+     "truth_in_range": 1.0
+    },
+    "R1y": {
+     "mean_probe_supply": 7.2,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 1.4,
+     "median_width": 53.0,
+     "n": 20,
+     "tie_rate": 0.75,
+     "top1": 0.15,
+     "truth_in_range": 1.0
+    },
+    "R2k1": {
+     "cases_with_truth_out": 0,
+     "median_width": 53.0,
+     "n": 20,
+     "top1_rate": 0.175,
+     "trials": 200,
+     "truth_out_rate": 0.0
+    },
+    "R2k2": {
+     "cases_with_truth_out": 1,
+     "median_width": 53.0,
+     "n": 14,
+     "top1_rate": 0.1857,
+     "trials": 140,
+     "truth_out_rate": 0.0071
+    },
+    "R3": {
+     "mean_probe_supply": 7.15,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0,
+     "median_width": 54.0,
+     "n": 20,
+     "tie_rate": 0.65,
+     "top1": 0.15,
+     "truth_in_range": 1.0
+    },
+    "R3+R1d": {
+     "mean_probe_supply": 7.15,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.7,
+     "median_width": 53.0,
+     "n": 20,
+     "tie_rate": 0.6,
+     "top1": 0.2,
+     "truth_in_range": 0.95
+    }
+   }
+  },
+  "raw": {
+   "10": {
+    "B0": {
+     "mean_probe_supply": 5.1,
+     "mean_questions": 4.25,
+     "mean_typed_scored": 0,
+     "median_width": 15.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.75,
+     "truth_in_range": 1.0
+    },
+    "R1d": {
+     "mean_probe_supply": 5.1,
+     "mean_questions": 4.25,
+     "mean_typed_scored": 0.2,
+     "median_width": 15.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.7,
+     "truth_in_range": 1.0
+    },
+    "R1y": {
+     "mean_probe_supply": 5.1,
+     "mean_questions": 4.25,
+     "mean_typed_scored": 0.45,
+     "median_width": 14.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.6,
+     "truth_in_range": 1.0
+    },
+    "R2k1": {
+     "cases_with_truth_out": 0,
+     "median_width": 15.0,
+     "n": 20,
+     "top1_rate": 0.695,
+     "trials": 200,
+     "truth_out_rate": 0.0
+    },
+    "R2k2": {
+     "cases_with_truth_out": 1,
+     "median_width": 15.0,
+     "n": 14,
+     "top1_rate": 0.7286,
+     "trials": 140,
+     "truth_out_rate": 0.0286
+    },
+    "R3": {
+     "mean_probe_supply": 5.1,
+     "mean_questions": 4.25,
+     "mean_typed_scored": 0,
+     "median_width": 14.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.6,
+     "truth_in_range": 1.0
+    },
+    "R3+R1d": {
+     "mean_probe_supply": 5.1,
+     "mean_questions": 4.25,
+     "mean_typed_scored": 0.2,
+     "median_width": 14.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.55,
+     "truth_in_range": 1.0
+    }
+   },
+   "30": {
+    "B0": {
+     "mean_probe_supply": 7.1,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0,
+     "median_width": 33.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.5,
+     "truth_in_range": 1.0
+    },
+    "R1d": {
+     "mean_probe_supply": 7.1,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.45,
+     "median_width": 33.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.45,
+     "truth_in_range": 1.0
+    },
+    "R1y": {
+     "mean_probe_supply": 7.1,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.95,
+     "median_width": 33.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.45,
+     "truth_in_range": 1.0
+    },
+    "R2k1": {
+     "cases_with_truth_out": 1,
+     "median_width": 33.0,
+     "n": 20,
+     "top1_rate": 0.45,
+     "trials": 200,
+     "truth_out_rate": 0.015
+    },
+    "R2k2": {
+     "cases_with_truth_out": 1,
+     "median_width": 36.0,
+     "n": 14,
+     "top1_rate": 0.4929,
+     "trials": 140,
+     "truth_out_rate": 0.0214
+    },
+    "R3": {
+     "mean_probe_supply": 7.1,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0,
+     "median_width": 33.0,
+     "n": 20,
+     "tie_rate": 0.0,
+     "top1": 0.65,
+     "truth_in_range": 1.0
+    },
+    "R3+R1d": {
+     "mean_probe_supply": 7.1,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.45,
+     "median_width": 33.0,
+     "n": 20,
+     "tie_rate": 0.0,
+     "top1": 0.6,
+     "truth_in_range": 1.0
+    }
+   },
+   "60": {
+    "B0": {
+     "mean_probe_supply": 7.2,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0,
+     "median_width": 56.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.35,
+     "truth_in_range": 1.0
+    },
+    "R1d": {
+     "mean_probe_supply": 7.2,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.7,
+     "median_width": 56.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.3,
+     "truth_in_range": 1.0
+    },
+    "R1y": {
+     "mean_probe_supply": 7.2,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 1.4,
+     "median_width": 56.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.3,
+     "truth_in_range": 1.0
+    },
+    "R2k1": {
+     "cases_with_truth_out": 0,
+     "median_width": 56.0,
+     "n": 20,
+     "top1_rate": 0.3,
+     "trials": 200,
+     "truth_out_rate": 0.0
+    },
+    "R2k2": {
+     "cases_with_truth_out": 0,
+     "median_width": 54.0,
+     "n": 14,
+     "top1_rate": 0.3,
+     "trials": 140,
+     "truth_out_rate": 0.0
+    },
+    "R3": {
+     "mean_probe_supply": 7.15,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0,
+     "median_width": 55.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.45,
+     "truth_in_range": 1.0
+    },
+    "R3+R1d": {
+     "mean_probe_supply": 7.15,
+     "mean_questions": 5.4,
+     "mean_typed_scored": 0.7,
+     "median_width": 54.0,
+     "n": 20,
+     "tie_rate": 0.05,
+     "top1": 0.4,
+     "truth_in_range": 1.0
+    }
+   }
+  }
+ },
+ "today": "2026-09-16",
+ "verdicts": {
+  "percent": {
+   "R1d": "no_benefit",
+   "R1y": "no_benefit",
+   "R3": "no_benefit",
+   "R3+R1d": "no_benefit"
+  },
+  "raw": {
+   "R1d": "no_benefit",
+   "R1y": "no_benefit",
+   "R3": "no_benefit",
+   "R3+R1d": "no_benefit"
+  }
+ }
+}
diff --git a/docs/research/rectification_typed_event_scoring_2026_09_29.md b/docs/research/rectification_typed_event_scoring_2026_09_29.md
new file mode 100644
index 00000000..219ad759
--- /dev/null
+++ b/docs/research/rectification_typed_event_scoring_2026_09_29.md
@@ -0,0 +1,104 @@
+# 打字经历按选择题规则计分：离线研究（2026-09-29）
+
+- 任务书：`docs/tasks/TASK-rectification-typed-event-scoring-research-20260929.md`；进度：`docs/tasks/PROGRESS-rectification-typed-event-research-20260929.md`
+- 分支：`codex/rectification-typed-event-research-20260929`，基线 `origin/staging` @ `56722063`。
+- 性质：**离线测量，不改线上默认值。** 研究补丁（R3 的 `_existence_blocked_years`）只在一次调用内替换，调用后与脚本末尾都断言已恢复原函数。
+- 数据：`references/real_case_calibration/minute_rectification_holdout_v4.json`，20 例公开 Rodden-AA。v4 的事件是 59 件日精度、84 件年精度，**没有月精度**；每例留一件作 holdout，训练集为日精度 57 件、年精度 67 件。**这是开放集回放，不是盲测，下面的数字不是准确率。**
+- 口径：ayanamsa `raman`，node mode `mean`，步长 2 分钟，半径 ±10 / ±30 / ±60；六题回放（`ASK_COUNT = 6`，按真值作答）；交付区间 = 未淘汰且落后头名不足 8 分的簇的并集。两种先验都报：`raw`（引擎行原始分，09-14 / 09-26 研究口径）与 `percent`（公开代表按正比例换成整数百分比，线上 `relative_support` 口径）。
+- 复跑：`PYTHONHASHSEED=0 python3 scripts/research/typed_event_as_probe.py`（约 1.5 分钟）→ `docs/research/rectification_typed_event_scoring_2026_09_29.json`。同机两次运行 JSON 逐字节一致。
+- 判门（任务书）：三档**同时**满足「头名命中不降、真值在区间不降、宽度中位下降」才算有收益。
+
+## 结论
+
+| 项 | 一句话结论 | 是否建议立实现单 |
+| --- | --- | --- |
+| R0 学业质量题措辞（BUG-1088） | 已修：按经历类型称呼（升学 / 学业变动 / 学业中断），年精度经历不再显示「1 月」；计分输出逐字节不变，已按 ERR-110 用新路径重新冻结 | 已在本分支实现 |
+| R1 打字经历当作已答「是」的选择题 | **不过门。** 打字经历绝大多数分不开候选：±10 / ±30 / ±60 下，57 件日精度训练经历只有 4 / 9 / 14 件（7% / 16% / 25%）能把代表候选分到两边；宽度中位三档都不变，头名在 raw 口径下三档都降 | **不建议** |
+| R2 经历年月记错 ±1–3 个月 | 挤出真值的比例 0–2.9%，与 09-26 答错两题（±30 约 7%）相比不高；但 R1 本身没有收益，R2 不改变结论 | 不适用 |
+| R3 挡题范围缩到「同领域同年」 | **不过门，但宽窗有信号。** raw 口径下 ±30 头名 0.50 → 0.65、±60 0.35 → 0.45；±10 头名 0.75 → 0.60（宽度 15 → 14）。宽度三档基本不变。60 格中有 45 格实际问到的六题因此换了 | **不建议直接实现**；若要继续，需另立研究，先解释 ±10 的头名下降 |
+
+**给产品的话：** 「打字说的经历不参与收窄」在代码里确实是两条不同的通道，但把打字经历改成按选择题规则计分**几乎没有用处**：一件你记得清清楚楚的事，多半恰好落在所有候选时刻都一样的那段大运里，本来就分不开候选。能分开候选的，是系统专门挑出来的「边界月份」选择题。这也解释了 09-26 研究 R3c 的发现：边界月份里只有 3–4.5% 能把代表候选分开。所以止血单「门开后不再索要经历、选择题问完就出卡」的方向是对的，本研究不支持再往打字经历上加计分。
+
+## R1 · 打字经历当作已答选择题
+
+**方法。** 对每件训练经历，用出题器的同一个判定函数 `event_probes._evaluate_contexts`（代表候选、签名聚类、同一套集合版本），以经历的领域和年月（日精度取所在月；R1y 的年精度取 `month=None`，即现有「整年」判定）生成一道「题」，按「是」作答（±2，强冲突计数同线上），然后照常回放六道选择题。函数返回 `None` 的经历（候选在那个月没有分边）不计分。
+
+**能分开候选的经历（每档 20 例合计）：**
+
+| 半径 | 日精度训练经历 | 能分边的 | 年精度训练经历 | 能分边的 |
+| --- | ---: | ---: | ---: | ---: |
+| ±10 | 57 | 4 | 67 | 5 |
+| ±30 | 57 | 9 | 67 | 10 |
+| ±60 | 57 | 14 | 67 | 14 |
+
+**raw 口径：**
+
+| 半径 | 方案 | 头名命中 | 真值在区间 | 宽度中位 | 并列率 | 每例提问 | 每例计分经历 |
+| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| ±10 | B0 | 0.75 | 1.00 | 15 | 0.05 | 4.25 | 0 |
+| ±10 | R1d | 0.70 | 1.00 | 15 | 0.05 | 4.25 | 0.20 |
+| ±10 | R1y | 0.60 | 1.00 | 14 | 0.05 | 4.25 | 0.45 |
+| ±30 | B0 | 0.50 | 1.00 | 33 | 0.05 | 5.40 | 0 |
+| ±30 | R1d | 0.45 | 1.00 | 33 | 0.05 | 5.40 | 0.45 |
+| ±30 | R1y | 0.45 | 1.00 | 33 | 0.05 | 5.40 | 0.95 |
+| ±60 | B0 | 0.35 | 1.00 | 56 | 0.05 | 5.40 | 0 |
+| ±60 | R1d | 0.30 | 1.00 | 56 | 0.05 | 5.40 | 0.70 |
+| ±60 | R1y | 0.30 | 1.00 | 56 | 0.05 | 5.40 | 1.40 |
+
+**percent 口径（线上先验尺度）：**
+
+| 半径 | 方案 | 头名命中 | 真值在区间 | 宽度中位 | 并列率 |
+| --- | --- | ---: | ---: | ---: | ---: |
+| ±10 | B0 | 0.50 | 1.00 | 11 | 0.40 |
+| ±10 | R1d | 0.45 | 1.00 | 11 | 0.40 |
+| ±10 | R1y | 0.35 | 1.00 | 11 | 0.45 |
+| ±30 | B0 | 0.40 | 1.00 | 33 | 0.55 |
+| ±30 | R1d | 0.40 | 1.00 | 33 | 0.55 |
+| ±30 | R1y | 0.40 | 1.00 | 33 | 0.45 |
+| ±60 | B0 | 0.15 | 1.00 | 53 | 0.75 |
+| ±60 | R1d | 0.20 | 1.00 | 53 | 0.75 |
+| ±60 | R1y | 0.15 | 1.00 | 53 | 0.75 |
+
+**读法。**
+
+1. 每例平均只有 0.2–0.7 件日精度经历能计分，宽度中位三档都不动。多出来的 ±2 只改排序，而且往往改错：raw 口径下头名三档都下降。
+2. 真值从未被挤出区间（真值在区间保持 1.00）：单件经历的 ±2 不够 8 分淘汰线。
+3. percent 口径的并列率高（0.40–0.75），是整数百分比把相近原始分压成同一个数所致，与本研究的方案无关；这是线上先验尺度（BUG-560）的另一面，只记录，不处理。
+
+## R2 · 经历年月记错 ±1–3 个月
+
+每例每档随机挑 1 件或 2 件日精度训练经历，把月份挪 ±1–3 个月（种子 `20260929`，每格重复 10 次），按 R1d 计分后回放。k=2 只在日精度训练经历 ≥2 的 14 例上跑。
+
+| 口径 | 半径 | k | 回放次数 | 挤出真值比例 | 至少一次挤出的例子 | 宽度中位 |
+| --- | --- | ---: | ---: | ---: | ---: | ---: |
+| raw | ±10 | 1 / 2 | 200 / 140 | 0.0% / 2.9% | 0 / 1 | 15 / 15 |
+| raw | ±30 | 1 / 2 | 200 / 140 | 1.5% / 2.1% | 1 / 1 | 33 / 36 |
+| raw | ±60 | 1 / 2 | 200 / 140 | 0.0% / 0.0% | 0 / 0 | 56 / 54 |
+| percent | ±10 | 1 / 2 | 200 / 140 | 0.0% / 2.9% | 0 / 1 | 11 / 12 |
+| percent | ±30 | 1 / 2 | 200 / 140 | 1.5% / 2.9% | 1 / 1 | 33 / 34 |
+| percent | ±60 | 1 / 2 | 200 / 140 | 0.0% / 0.7% | 0 / 1 | 53 / 53 |
+
+挤出率低，原因同 R1：挪错后的月份多半也分不开候选，等于没计分。R2 不改变 R1 的结论。
+
+## R3 · 挡题范围缩到「同领域同年」
+
+线上 `_existence_blocked_years` 会把已入账年份及同领域相邻年份（`EXISTENCE_NEARBY_YEARS`）的存在题挡掉。研究补丁改为只挡「同领域同一年」，其余不变。
+
+| 口径 | 半径 | B0 头名 / 宽度 | R3 头名 / 宽度 | R3+R1d 头名 / 宽度 | R3 判定 |
+| --- | --- | --- | --- | --- | --- |
+| raw | ±10 | 0.75 / 15 | 0.60 / 14 | 0.55 / 14 | 头名降 |
+| raw | ±30 | 0.50 / 33 | 0.65 / 33 | 0.60 / 33 | 宽度未降 |
+| raw | ±60 | 0.35 / 56 | 0.45 / 55 | 0.40 / 54 | 过 |
+| percent | ±10 | 0.50 / 11 | 0.45 / 11 | 0.40 / 11 | 头名降 |
+| percent | ±30 | 0.40 / 33 | 0.50 / 31 | 0.50 / 30 | 过 |
+| percent | ±60 | 0.15 / 53 | 0.15 / 54 | 0.20 / 53 | 宽度升 |
+
+真值在区间：除 percent ±60 R3+R1d 为 0.95 外全部 1.00。出题供给（截断到 `MAX_PROBES = 8` 之后）几乎不变（每例 5.1 / 7.1 / 7.2 → 5.1 / 7.1 / 7.15），但 60 格中有 45 格实际问到的前六题换了：放开相邻年份后，出题器会挑到别的年份。
+
+**读法。** 宽窗（±30 / ±60）头名有 +0.10 到 +0.15 的改善，但样本只有 20 例（每例 0.05），±10 头名下降同样幅度，三档不同时过门。按任务书的门槛判 `no_benefit`。若产品想追，下一步应是另立研究：只在窗口 ≥30 分钟时放开，并先解释 ±10 下降是换题带来的随机波动还是系统性问题。
+
+## 偏离与说明
+
+- 基线头名与 09-26 研究略有差异（raw B0：本文 0.75 / 0.50 / 0.35，09-26 为 0.80 / 0.55 / 0.40；宽度 15 / 33 / 56 完全一致）。本脚本自己实现了回放循环（为了在六题之前插入经历计分），`top1_hit` 用的是相邻合并后的簇；各方案之间用的是同一条回放链路，比较有效，但数字不要与 09-26 逐格对照。
+- 任务书以「记得到月」为主设想；v4 没有月精度事件，日精度事件按所在月判定，等价于月精度判定。
+- 任务书写的 R1 年精度口径「只在跨年边界时计分」，实现为出题器现有的整年判定（`_evaluate_contexts(month=None)`，事件日取 7 月 1 日），它已经只在候选整年激活状态不同时才分边。
diff --git a/docs/tasks/PROGRESS-rectification-typed-event-research-20260929.md b/docs/tasks/PROGRESS-rectification-typed-event-research-20260929.md
new file mode 100644
index 00000000..c9fd4cbc
--- /dev/null
+++ b/docs/tasks/PROGRESS-rectification-typed-event-research-20260929.md
@@ -0,0 +1,51 @@
+# PROGRESS · 打字经历按选择题规则计分（离线研究）+ 学业质量题措辞（2026-09-29）
+
+- 任务书：`docs/tasks/TASK-rectification-typed-event-scoring-research-20260929.md`
+- 执行：Claude（直接执行模式，fork 子代理），分支 `codex/rectification-typed-event-research-20260929`，基线 `origin/staging` @ `56722063`。未推送。
+- 研究报告：`docs/research/rectification_typed_event_scoring_2026_09_29.md` + `.json`
+
+## 开工预检
+
+- `python3 scripts/pre_work_check.py --remote-timeout 8 --command-timeout 45`：exit 0；python_runtime / fragment_scan / external_engine_adapters / focused_tests 全部 ok，remote_visibility `verified`。
+- 读 `docs/research/pre_work_error_ledger.md` ERR-110（冻结文件改动须重新冻结）、ERR-111（固定 `PYTHONHASHSEED`）。本机无 `.venv`，全部用系统 `python3`。
+
+## R0 · 学业质量题措辞（BUG-1088）
+
+- `scripts/rectification/event_probes.py`：`_display_date_label` 对 `precision == "year"` 不取月；`_quality_user_meaning` 学业分支按 `event_kind` 称呼（`EDUCATION_QUALITY_EVENT_NAME`：升学 / 学业变动 / 学业中断，兜底「学业经历」），句式「…那次X，结果更接近如愿、将就调剂、发挥失常还是说不清」。四个选项标签未改（仍含「录取」字样，属选项文案，留待产品决定）。
+- **计分身份不变**：`_event_month` 未改，`candidate_split_hash`、题目 `month` 字段仍带存储的月份；新增测试锁住这一点。
+- 同机 A/B（`scripts/research/quality_wording_ab.py`，`PYTHONHASHSEED=0`，v4 前 4 例 ±10 + 2 个虚构出生带学业质量事件）：基线连跑两次逐字节一致；改后对比，**只有 2 个叶子字段类型变化**：`user_meaning`（2 处）与 `display_date_label`（2 处），均在虚构例的 `known_event_quality` 题上；引擎行分数、公开候选决策、全部题目的 id / split hash / outcomes 逐字节相同。
+  - `2010 年 1 月那次上大学…` → `2010 年那次学业中断…`（年精度 + interruption）
+  - `2011 年 1 月那次上大学…` → `2011 年那次升学…`（年精度 + start）
+- 重新冻结（ERR-110，不覆盖旧记录）：
+  - `scripts/research/sealed_holdout_rerun.py` `FREEZE/REPORT` → `docs/research/sealed_holdout_rerun_quality_wording_2026_09_29.{freeze.json,json}`；重放 20 例，metrics 与 20 条 trials 与 09-21 记录**完全相同**（top1 0.45 / top3 0.50 / MAE 6.45）。
+  - `scripts/research/reported_offset_sweep.py` `FREEZE/REPORT` → `docs/research/reported_offset_quality_wording_2026_09_29.{freeze.json,json}`；900 trials 与 summary 与 09-21 **完全相同**。
+  - `references/rectification_sealed_holdout.v1.json`：`current_tree_scorer`、`current_tree_fixed_protocol_rerun`、`current_tree_reported_offset_replay` 指向新记录；旧的 09-21 记录移入 `previous_fixed_protocol_rerun` / `previous_reported_offset_replay`；`evaluated_on` 2026-09-29。遗留 12 文件哈希 `c913c6f0…` 不变，只有扩展身份（event_probes.py 与两份研究脚本）变化。
+  - `tests/test_sealed_holdout_contract_freshness.py` 的 `IDENTITY_FORBIDDEN_PREFIXES` 补两条新前缀（同 09-21 先例）。
+- 新测试：`tests/test_rectification_event_probes.py::QualityWordingTests`（4 条：三种类型不叫上大学、年精度无月、月精度保留月、split 月份不变）。
+
+## R1–R3 · 离线研究（BUG-1089）
+
+脚本 `scripts/research/typed_event_as_probe.py`，`PYTHONHASHSEED=0` 连跑两次 JSON 逐字节一致（各 1 分 25 秒）。结论全部 `no_benefit`，**不建议立实现单**。数字见研究报告；要点：
+
+| 项 | raw 口径（±10 / ±30 / ±60） | 判定 |
+| --- | --- | --- |
+| B0 头名 / 宽度 | 0.75 / 0.50 / 0.35；15 / 33 / 56 | 基线 |
+| R1d 头名 / 宽度 | 0.70 / 0.45 / 0.30；15 / 33 / 56 | no_benefit |
+| R1y 头名 / 宽度 | 0.60 / 0.45 / 0.30；14 / 33 / 56 | no_benefit |
+| R3 头名 / 宽度 | 0.60 / 0.65 / 0.45；14 / 33 / 55 | no_benefit（±10 头名降） |
+| 能分边的日精度训练经历 | 4 / 9 / 14（共 57 件） | — |
+| R2 挤出真值（k=1 / k=2） | 0–1.5% / 2.1–2.9% | — |
+
+percent 口径结论相同（判定全部 `no_benefit`）。
+
+## 测试
+
+- 定向：`tests/test_rectification_validation_integrity_gate.py`、`tests/test_sealed_holdout_contract_freshness.py`、`tests/test_reported_offset_research.py`、`tests/test_rectification_event_probes.py`：83 passed（改前重新冻结前为 7 失败，全部是冻结身份不匹配）。
+- 快速门 `python3 scripts/run_quality_gate.py --profile quick`（系统 python3，本机无 `.venv`）：JSON / 审计 / 碎片 / BPHS 各步通过；pytest **1001 passed, 1 skipped**（含上面四个文件）。随后 `npm test` 步骤 exit 127：本工作树没有 `frontend/node_modules`，npm 找不到测试依赖。**前端三步（npm test / lint / build）未跑，记为环境缺口，不算通过。** 本轮没有改任何前端文件。
+- 隐私：`tests/test_repo_privacy_markers.py` 通过。
+
+## 偏离
+
+- 基线回放头名与 09-26 研究差 0.05（宽度一致），原因见研究报告「偏离与说明」；方案之间可比，不与 09-26 逐格对照。
+- v4 无月精度事件，日精度按所在月判定。
+- 未改质量题四个选项标签；前端 `collection-question-pool.ts` 也有「上大学」措辞（采集题示例），不在本单范围，未改。
diff --git a/docs/tasks/README.md b/docs/tasks/README.md
index a7582602..cf54d7b6 100644
--- a/docs/tasks/README.md
+++ b/docs/tasks/README.md
@@ -254,7 +254,7 @@
 | `TASK-self-edit-avatar-menu-20260928.md` | `PROGRESS-self-edit-avatar-menu-20260928.md` | **本人可编辑 + 各页头像菜单 + 报告页去说明**：`/people` 本人可编辑出生资料（BUG-1081，`ab6c55f8` 漏掉）；次级页头像弹同一账户菜单、弹窗项跳 `/?account=`；我的报告删顶部说明与统计 | 已实现待验收（分支 `codex/self-edit-avatar-menu-20260928`，未推送） | BUG-1081 |
 | `TASK-mobile-chart-and-confirmed-edit-20260929.md` | `PROGRESS-mobile-chart-confirmed-edit-20260929.md` | **手机星盘 + 确认时间可改 + 天空定格**：iPhone 星盘被宽表撑出屏幕（BUG-1083）；confirmed 状态改声明字段也重置排盘时间（推翻 BUG-264 约定）；「那一刻的天空」改为重放汇聚→定格→右上角分享；去掉报告星图封面 | 已验收（Claude 2026-09-29），已推 staging | BUG-1083 |
 | `TASK-rectification-futile-collect-stop-20260929.md` | — | **生时校正停掉无效补经历循环（止血）**：打字经历不收窄（BUG-560 后果），流程却一路索要，真机 22 件整窗不动；采集只为开闸、门开后只问点选卡问完即出卡；交付正文去吻合率；多段时旁白误报「范围没变」；交付轮带采集题 | 待领取 | BUG-1084～1087 |
-| `TASK-rectification-typed-event-scoring-research-20260929.md` | — | **打字经历按选择题规则计分（离线研究，不上线）**：计分通道不对称 + 已入账年份挡题；R0 学业质量题措辞 / 年精度显示成 1 月（冻结文件，需重新冻结） | 待领取 | BUG-1088、1089 |
+| `TASK-rectification-typed-event-scoring-research-20260929.md` | `PROGRESS-rectification-typed-event-research-20260929.md` | **打字经历按选择题规则计分（离线研究，不上线）**：计分通道不对称 + 已入账年份挡题；R0 学业质量题措辞 / 年精度显示成 1 月（冻结文件，需重新冻结） | 待验收（Claude 子代理直接执行，分支 `codex/rectification-typed-event-research-20260929`，未推送；R1–R3 均 no_benefit） | BUG-1088、1089 |
 | `TASK-serif-headings-20260928.md` | `PROGRESS-serif-headings-20260928.md` | **全站标题改用自托管宋体、正文保持黑体**：产品推翻 BUG-737「CJK 不用衬线」结论（保留「声明的字体必须可加载」「不落系统宋体」两条）；Noto Serif SC SemiBold 按通用规范汉字表 6500 字 unicode-range 切片自托管（改名 Jyotisha Serif SC），swap 不 preload；首页宋体流量 ≤300 KB；先于天空封面单 | 已实现待验收（分支 `codex/serif-headings-20260928`，未推送） | 不开新 BUG；BUG-737 追加说明 |
 | `TASK-cend-ui-claude-alignment-20260916.md` | `PROGRESS-cend-ui-r1/r2/r3-20260916.md` | **C 端界面向 claude.ai 产品界面对齐（三轮串行 R1→R2→R3，都动 `globals.css`，不得并行）**：根因是 `frontend/CLAUDE_DESIGN.md` 扒的是 **claude.com 营销官网**，它自己在 Known Gaps 里写明 claude.ai 产品界面不在范围内，而 `DESIGN.md:3` 把它当成了产品界面的实现契约。**R1**：`--font-display` 里 Tiempos Headline / StyreneB **从未加载**（无 `@font-face`、`public/` 无字体、`layout.tsx` 只 vendor 了 Inter），中文标题全站落到 **宋体 / SimSun**，波及 20 处含助手回答的 h2/h3（BUG-737）；亮色强调色 `#85432f` 与暗色 `#d78064` 不同源，产品拍板亮色换 **Claude coral `#cc785c`**，**易漏点**是 `globals.css:16` 的 `--color-ring` 硬编码在 `@theme inline` 里不跟随 `:root`，另有第四个 `:root` 亮色块（`:4358`）必须同步（BUG-738）；`.composer-footer` 常驻 44px + 顶栏 68px + `--composer-reserve` 148px，每屏固定吃掉 216px，模型选择器移进输入框内部、删掉底栏、顶栏收到 46px 并删「分析对象」副标题。**R2**：空状态是营销落地页（hero 卡 + 两张 132px 入口大卡 + 3 列 156px 主题卡），输入框被压在 **800px 以上**内容之下，重排成「问候 + 居中输入框 + 两枚入口 pill + 一排 chip」。**R3**：侧栏两个 `<details>` 拍平成一条「最近」、星盘的两个入口（侧栏分组 + 账户菜单）收敛到一处、删掉逐条助手头像。**决策记录 D3 推翻 DESIGN.md「报告强调色与应用同源」一句**（报告刻意保留深棕）。原型图 https://claude.ai/code/artifact/da275da6-2954-4f50-99aa-32bb8694d38b（三套画面 + 明暗，页面标题就是建议字体栈的实际渲染）。环境缺口：无登录态无 Chrome，四项真机观感留 `docs/testing/`。BUG 段 737–738 | 待领取 | — |
 | `TASK-cend-surfaces-claude-alignment-20260916.md` | `PROGRESS-cend-shell-20260916.md`、`PROGRESS-cend-report-20260916.md`、`PROGRESS-cend-rectification-20260916.md`、`PROGRESS-cend-chart-eph-20260916.md` | **次级页面对齐（上一单的续篇，R4→R5/R6，R7、R8 可并行）**：星盘 `/chart`、星历 `/ephemeris`、报告 `/reports` **各是脱离 app 外壳的独立全屏页**，顶部只有一个「返回对话」链接、侧栏整个消失，且三家各写了一套一模一样的 `*-shell`/`*-topbar`/`*-hero` 骨架——与上一单 E5 同根因（营销站 band 结构被套到产品界面）。**R4** 抽只读导航外壳 `AppNavRail`（只用现成的 `GET /api/sessions` + `GET /api/account`，会话行走 `sessionHref` 跳 `/?c=<uuid>`；**刻意不带**重命名/删除/收藏/归档——那套连着 `Home()` 的乐观更新与回滚，搬过来会撞 useState 增长门禁）。**R5** 星盘五 tab 下划线化 + 参数合表 + 行星表横向滚动；星历日期导航改 `‹ 日期 ›`。**R6** 报告中心卡片网格改行式列表；阅读页加常驻目录。**R7** 生时校正把可信区间从盘面板标题行提成常驻条（窄屏 `.is-compact` 下盘面板是 overlay，现在默认看不到区间），五个 `technique-audit` 折叠块收成两段。**R8** 设置内容区收窄（880px 弹窗里表单铺了 690px）、套餐卡三修饰符收敛成两态。**已解锁**：原挡路的设置单已于 `111b4a84`（BUG-698）合入。**两条不得回退**：BUG-698 的 `@supports (height: 1dvh)` 写法（重复声明回退会被 Lightning CSS 折叠）、BUG-616/617 的报告盘面 grid 实现。默认不占 BUG 号 | **R4–R8 全部已实现并验收合入** | R4：抽出 `AppNavRail`（只读，两个 GET，零写操作）+ `SecondaryShell`，三个次级页并入 app 外壳并删掉各自的 shell/topbar/hero；四个路由渲染标记**完全不变**（`/` `/chart` `/ephemeris` 仍 Static）；CSS gzip −0.25%。`/reports/[reportId]` 留给 R6 与目录一起做。两处自身健壮性问题被测试抓到：`usePathname()` 可为 null、`fetch` 可能不存在。差点弄丢 BUG-717 的 eyebrow 文案（已放回）。R8：表单分区收窄到 440px（列表分区不变）、套餐卡三修饰符收敛成互斥的 `is-current` / `is-recommended`，`--highlighted` 删除改为滚动定位；手机端 `order:-1` 改挂 `[data-plan-alias]`（版位不是状态）。测试 3350→3354（净增 4），失败清单与基线逐条一致；`/` 仍 Static；我的干净构建实测 CSS gzip −3 字节。**遗留待产品拍板**：`?plan=` 深链现在完全没有视觉指向，只有滚动位置。R6：报告中心卡片网格改行式列表、阅读页并入外壳并把目录挪到右侧常驻。**任务书 E10 过期**——目录在 `cfcd369d` 就已存在，本轮是挪位置定稿而非从零加。挂外壳带出一个真实打印风险已处理：`.chat-app`/`.chat-panel` 是 `height:100%;overflow:hidden`，裸 `window.print()` 会把九节报告裁成一页，阅读页因此多挂一条只在挂载期生效的 print 样式解锁外壳。「生成中的分节进度」做不了——`REPORT_LIST_COLUMNS` 不返回节数，按 VOICE.md 不许前端编。R7：区间常驻条与盘面折叠收敛。**任务书 E9 也不准确**——对话区顶部早有常驻条 `RectificationTimeline` 且窄屏可见，真正只在盘面标题行的是**代表分钟**；因此没另造第二条，在既有条上补齐代表分钟与已答题数（与盘面同一次 `workingRectificationTime()` 调用）。折叠块实际是 **8 个**不是 5 个。**触发让步顺序第 5 条**：收窄进度未做——服务端无该字段，且 `candidate_range` 会放宽（BUG-572），前端相减会把一次放宽报成收窄，已写进 `BLOCKED.md`。顺带修掉一个**静默失效的旧断言**（`slice(indexOf(A), indexOf(B))` 在 B 改名后变成几乎整份文件，四条 `doesNotMatch` 假通过）|
diff --git a/scripts/research/typed_event_as_probe.py b/scripts/research/typed_event_as_probe.py
new file mode 100644
index 00000000..b00c3cad
--- /dev/null
+++ b/scripts/research/typed_event_as_probe.py
@@ -0,0 +1,391 @@
+#!/usr/bin/env python3
+"""Offline research for TASK-rectification-typed-event-scoring-research-20260929.
+
+Question: should a dated typed event ("I changed jobs in 2019-10") move the
+candidate scores the same way an answered existence probe does?
+
+Today an answered dated probe moves every candidate by ±2 through the
+Vimshottari/Narayana boundary-month split (`event_probes._evaluate_contexts`),
+while a typed event only reaches the engine prior, and the frontend skips
+`classified_from === "evidence"` yes answers (`core/build-state.ts`).
+
+This script replays the v4 open holdout (20 public Rodden-AA cases, 7–8 events
+each; 59 day-precision and 84 year-precision events, no month-precision) with
+the same six-probe replay used by the 09-14 / 09-16 / 09-26 studies, and adds
+research arms:
+
+* ``B0``   baseline: prior, then the first six public probes answered from truth.
+* ``R1d``  each training typed event with day precision is scored as an answered
+           ``yes`` probe (split from `_evaluate_contexts` at that year/month),
+           then the same six probes.
+* ``R1y``  as R1d, plus year-precision events scored with the year-level split
+           (`_evaluate_contexts(month=None)`, the existing year probe path).
+* ``R2k``  R1 with k ∈ {1, 2} typed day events moved by ±1–3 months (seeded);
+           reports how often the truth is pushed out of the delivered range.
+* ``R3``   `_existence_blocked_years` narrowed to "same domain, same year"
+           (probe supply count), alone and combined with R1d.
+
+Two prior scales are reported: ``raw`` (engine row score, the research-lib
+convention of the earlier studies) and ``percent`` (proportional percent over
+public representatives, the production `relative_support` convention).
+
+Nothing here changes a production default. Module attributes are patched for
+one call and restored; the end of `main` asserts they are the originals.
+This is an open-set replay, not a blind test: numbers are not accuracy.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import random
+import statistics
+import sys
+from contextlib import contextmanager
+from datetime import date
+from pathlib import Path
+from typing import Any, Iterator, Sequence
+
+ROOT = Path(__file__).resolve().parents[2]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+import scripts.rectification.event_probes as event_probes  # noqa: E402
+from scripts.active_rectification_event_engine import compute_candidate_static_contexts  # noqa: E402
+from scripts.rectification.candidate_contrast import cluster_contexts_by_signature  # noqa: E402
+from scripts.rectification.case_holdout import holdout_event_ids  # noqa: E402
+from scripts.rectification.refinement_packet import window_scan  # noqa: E402
+from scripts.rectification.scoring_service import (  # noqa: E402
+    build_event_contribution_matrix,
+    score_from_matrix,
+)
+from scripts.research.cluster_width_lib import (  # noqa: E402
+    SEPARATION_LEAD,
+    delivery_from_public,
+    merge_adjacent_traced,
+    public_from_clusters,
+    raw_signature_clusters,
+    still_valid_public,
+)
+from scripts.research.guided_collect_holdout_replay import (  # noqa: E402
+    TODAY,
+    _hhmm,
+    _minutes,
+    load_cases,
+    precision_gate,
+)
+from scripts.research.minute_resolution_sweep import scoring_request_for  # noqa: E402
+from scripts.research.probe_supply_after_six import (  # noqa: E402
+    ASK_COUNT,
+    apply_answer,
+    optimal_answer,
+    top1_hit,
+)
+
+RADII = (10, 30, 60)
+PRIORS = ("raw", "percent")
+REPORT_JSON = ROOT / "docs" / "research" / "rectification_typed_event_scoring_2026_09_29.json"
+SHIFT_SEED = 20260929
+SHIFT_REPEATS = 10
+TYPED_SOURCE = "typed_event_research"
+
+ORIGINAL_BLOCKED_YEARS = event_probes._existence_blocked_years
+
+
+@contextmanager
+def narrowed_blocking() -> Iterator[None]:
+    """R3: an already-known year blocks only itself in the same domain."""
+    event_probes._existence_blocked_years = lambda _domain, known_years: set(known_years)
+    try:
+        yield
+    finally:
+        event_probes._existence_blocked_years = ORIGINAL_BLOCKED_YEARS
+
+
+def _percent(scores: dict[str, float]) -> dict[str, float]:
+    total = sum(max(value, 0.0) for value in scores.values())
+    if total <= 0:
+        return {key: 0.0 for key in scores}
+    # Integer percent like `_relative_support_proportional` (largest remainder is not
+    # needed for ranking; plain rounding keeps the research copy independent).
+    return {key: float(round(max(value, 0.0) / total * 100)) for key, value in scores.items()}
+
+
+def _event_month(event: dict[str, Any]) -> int | None:
+    raw = str(event.get("date_start") or "")
+    return int(raw[5:7]) if len(raw) >= 7 and raw[4] == "-" else None
+
+
+def _event_year(event: dict[str, Any]) -> int | None:
+    raw = str(event.get("date_start") or "")
+    return int(raw[:4]) if raw[:4].isdigit() else None
+
+
+def _shift_month(event: dict[str, Any], months: int) -> dict[str, Any]:
+    year, month = _event_year(event), _event_month(event)
+    if year is None or month is None:
+        return event
+    index = year * 12 + (month - 1) + months
+    new_year, new_month = divmod(index, 12)
+    stamp = f"{new_year:04d}-{new_month + 1:02d}-15"
+    return {**event, "date_start": stamp, "date_end": stamp}
+
+
+def typed_event_probes(
+    request: dict[str, Any],
+    contexts: Sequence[dict[str, Any]],
+    *,
+    include_year: bool,
+    events: Sequence[dict[str, Any]] | None = None,
+) -> tuple[list[dict[str, Any]], dict[str, int]]:
+    """Score each training typed event through the probe split, answered ``yes``."""
+    full = [item for item in contexts if event_probes._context_time(item)]
+    full.sort(key=lambda item: event_probes._clock(str(event_probes._context_time(item))))
+    clusters = cluster_contexts_by_signature(full)
+    reps = [cluster["representative"] for cluster in clusters if event_probes._scoreable(cluster["representative"])]
+    if len(reps) < 2:
+        reps = [item for item in full if event_probes._scoreable(item)]
+    set_version = event_probes.candidate_set_version([cluster["times"] for cluster in clusters])
+    source_events = list(events if events is not None else request["events"])
+    holdout = set(holdout_event_ids(request["events"]))
+    stats = {"training_day": 0, "training_year": 0, "split_day": 0, "split_year": 0}
+    rows: list[dict[str, Any]] = []
+    for event in source_events:
+        if str(event.get("id")) in holdout:
+            continue
+        domain = event_probes.canonical_domain(event.get("domain"))
+        if domain not in event_probes.DOMAIN_CATALOG:
+            continue
+        precision = str(event.get("precision") or "")
+        year = _event_year(event)
+        if year is None:
+            continue
+        if precision in {"day", "month"}:
+            stats["training_day"] += 1
+            month = _event_month(event)
+            key = "split_day"
+        elif precision == "year" and include_year:
+            stats["training_year"] += 1
+            month = None
+            key = "split_year"
+        else:
+            continue
+        row = event_probes._evaluate_contexts(
+            reps,
+            birth_date=str(request["birth_date"]),
+            domain=domain,
+            year=year,
+            month=month,
+            source=TYPED_SOURCE,
+            clusters=clusters,
+            set_version=set_version,
+        )
+        if row is None:
+            continue
+        stats[key] += 1
+        rows.append(row)
+    return rows, stats
+
+
+def replay(
+    *,
+    rows: Sequence[dict[str, Any]],
+    contexts: Sequence[dict[str, Any]],
+    typed: Sequence[dict[str, Any]],
+    probes: Sequence[dict[str, Any]],
+    true_time: str,
+    prior_mode: str,
+) -> dict[str, Any]:
+    raw = raw_signature_clusters(contexts)
+    by_time = {stamp: row for row in rows if (stamp := _hhmm(row.get("time")))}
+    merged, _trace = merge_adjacent_traced(raw, by_time)
+    public = public_from_clusters(merged, rows)
+    reps = [str(row["time"])[:5] for row in public]
+    base = {stamp: float(row.get("score") or 0) for row in public if (stamp := _hhmm(row.get("time")))}
+    scores = _percent(base) if prior_mode == "percent" else dict(base)
+    conflicts = {time: 0 for time in reps}
+    eliminated: set[str] = set()
+    for probe in typed:
+        scores, conflicts, eliminated = apply_answer(scores, conflicts, eliminated, probe, "yes", reps)
+    asked = 0
+    for probe in list(probes)[:ASK_COUNT]:
+        answer = optimal_answer(probe, true_time)
+        if answer is None:
+            continue
+        asked += 1
+        scores, conflicts, eliminated = apply_answer(scores, conflicts, eliminated, probe, answer, reps)
+    posterior = [{**row, "score": scores.get(str(row["time"])[:5], row.get("score") or 0)} for row in public]
+    valid = still_valid_public(posterior, scores, eliminated, lead=SEPARATION_LEAD)
+    delivery = delivery_from_public(valid)
+    start, end = delivery.get("start"), delivery.get("end")
+    inside = start is not None and end is not None and _minutes(start) <= _minutes(true_time) <= _minutes(end)
+    active = [time for time in reps if time not in eliminated]
+    gate = precision_gate(valid, scores)
+    return {
+        "width": delivery.get("width"),
+        "truth_in_range": bool(inside),
+        "top1": bool(top1_hit(scores, active, true_time, merged)),
+        "tied": bool(gate["tied_for_first"]),
+        "questions": asked,
+        "typed_scored": len(typed),
+        "probe_supply": len(probes),
+    }
+
+
+def evaluate_case(case: dict[str, Any], radius: int) -> dict[str, Any]:
+    true_time = str(case["birth"]["time"])[:5]
+    request = scoring_request_for(case, radius)
+    contexts = compute_candidate_static_contexts(request)
+    built = build_event_contribution_matrix(request, static_contexts=contexts)
+    rows = score_from_matrix(request, built)
+    times = [stamp for row in rows if (stamp := _hhmm(row.get("time")))]
+
+    def probes_now() -> list[dict[str, Any]]:
+        return event_probes.discriminating_event_probes(
+            {**request, "refresh_probes": False, "asked_probe_keys": []}, built,
+            scan=window_scan(built), candidate_times=times, representative_time=true_time, today=TODAY,
+        )
+
+    probes = probes_now()
+    with narrowed_blocking():
+        probes_narrow = probes_now()
+    assert event_probes._existence_blocked_years is ORIGINAL_BLOCKED_YEARS
+
+    typed_day, stats = typed_event_probes(request, contexts, include_year=False)
+    typed_all, stats_all = typed_event_probes(request, contexts, include_year=True)
+
+    rng = random.Random(f"{SHIFT_SEED}:{case.get('case_id')}:{radius}")
+    holdout = set(holdout_event_ids(request["events"]))
+    day_events = [
+        index for index, event in enumerate(request["events"])
+        if str(event.get("precision")) in {"day", "month"} and str(event.get("id")) not in holdout
+    ]
+    shifted: dict[str, list[dict[str, Any]]] = {"1": [], "2": []}
+    for k in (1, 2):
+        if len(day_events) < k:
+            continue
+        for _ in range(SHIFT_REPEATS):
+            picks = rng.sample(day_events, k)
+            events = [
+                _shift_month(event, rng.choice((-3, -2, -1, 1, 2, 3))) if index in picks else event
+                for index, event in enumerate(request["events"])
+            ]
+            moved, _ = typed_event_probes(request, contexts, include_year=False, events=events)
+            shifted[str(k)].append({"typed": moved})
+
+    out: dict[str, Any] = {"case_id": case.get("case_id"), "radius": radius, "typed_stats": {**stats, **{
+        "training_year": stats_all["training_year"], "split_year": stats_all["split_year"]}},
+        "probe_supply": len(probes), "probe_supply_narrow": len(probes_narrow),
+        "asked_keys_changed_narrow": [p["semantic_key"] for p in probes[:ASK_COUNT]]
+        != [p["semantic_key"] for p in probes_narrow[:ASK_COUNT]],
+        "arms": {}}
+    for prior in PRIORS:
+        def run(typed: Sequence[dict[str, Any]], pool: Sequence[dict[str, Any]]) -> dict[str, Any]:
+            return replay(rows=rows, contexts=contexts, typed=typed, probes=pool, true_time=true_time, prior_mode=prior)
+
+        arms = {
+            "B0": run([], probes),
+            "R1d": run(typed_day, probes),
+            "R1y": run(typed_all, probes),
+            "R3": run([], probes_narrow),
+            "R3+R1d": run(typed_day, probes_narrow),
+        }
+        for k, trials in shifted.items():
+            results = [run(item["typed"], probes) for item in trials]
+            if results:
+                arms[f"R2k{k}"] = {
+                    "trials": len(results),
+                    "truth_out": sum(1 for row in results if not row["truth_in_range"]),
+                    "top1": sum(1 for row in results if row["top1"]),
+                    "median_width": statistics.median(row["width"] for row in results if row["width"] is not None),
+                }
+        out["arms"][prior] = arms
+    return out
+
+
+def summarize(rows: Sequence[dict[str, Any]], prior: str, radius: int, arm: str) -> dict[str, Any]:
+    subset = [row["arms"][prior][arm] for row in rows if row.get("radius") == radius and not row.get("error")
+              and arm in row["arms"][prior]]
+    n = len(subset)
+    if not n:
+        return {"n": 0}
+    if arm.startswith("R2k"):
+        trials = sum(item["trials"] for item in subset)
+        return {
+            "n": n,
+            "trials": trials,
+            "truth_out_rate": round(sum(item["truth_out"] for item in subset) / trials, 4),
+            "cases_with_truth_out": sum(1 for item in subset if item["truth_out"] > 0),
+            "top1_rate": round(sum(item["top1"] for item in subset) / trials, 4),
+            "median_width": statistics.median(item["median_width"] for item in subset),
+        }
+    return {
+        "n": n,
+        "top1": round(sum(item["top1"] for item in subset) / n, 4),
+        "truth_in_range": round(sum(item["truth_in_range"] for item in subset) / n, 4),
+        "median_width": statistics.median(item["width"] for item in subset if item["width"] is not None),
+        "tie_rate": round(sum(item["tied"] for item in subset) / n, 4),
+        "mean_questions": round(statistics.mean(item["questions"] for item in subset), 2),
+        "mean_typed_scored": round(statistics.mean(item["typed_scored"] for item in subset), 2),
+        "mean_probe_supply": round(statistics.mean(item["probe_supply"] for item in subset), 2),
+    }
+
+
+def verdict(summary: dict[str, Any], prior: str, arm: str) -> str:
+    """Gate: top1 not lower AND truth-in-range not lower AND median width lower, all radii."""
+    passes = []
+    for radius in RADII:
+        base = summary[prior][str(radius)]["B0"]
+        cand = summary[prior][str(radius)][arm]
+        if not cand.get("n"):
+            return "uncertain"
+        passes.append(
+            cand["top1"] >= base["top1"]
+            and cand["truth_in_range"] >= base["truth_in_range"]
+            and cand["median_width"] < base["median_width"]
+        )
+    return "benefit" if all(passes) else "no_benefit"
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--limit", type=int, default=0)
+    parser.add_argument("--json-out", default=str(REPORT_JSON))
+    args = parser.parse_args()
+    cases = load_cases()
+    if args.limit:
+        cases = cases[: args.limit]
+    rows: list[dict[str, Any]] = []
+    for case in cases:
+        for radius in RADII:
+            try:
+                rows.append(evaluate_case(case, radius))
+            except Exception as exc:  # noqa: BLE001
+                rows.append({"case_id": case.get("case_id"), "radius": radius, "error": f"{type(exc).__name__}: {exc}"})
+    arms = ("B0", "R1d", "R1y", "R3", "R3+R1d", "R2k1", "R2k2")
+    summary = {prior: {str(radius): {arm: summarize(rows, prior, radius, arm) for arm in arms}
+                       for radius in RADII} for prior in PRIORS}
+    verdicts = {prior: {arm: verdict(summary, prior, arm) for arm in ("R1d", "R1y", "R3", "R3+R1d")} for prior in PRIORS}
+    assert event_probes._existence_blocked_years is ORIGINAL_BLOCKED_YEARS
+    payload = {
+        "generated_for": "TASK-rectification-typed-event-scoring-research-20260929",
+        "today": TODAY.isoformat(),
+        "holdout": "references/real_case_calibration/minute_rectification_holdout_v4.json",
+        "ask_count": ASK_COUNT,
+        "separation_lead": SEPARATION_LEAD,
+        "shift_seed": SHIFT_SEED,
+        "shift_repeats": SHIFT_REPEATS,
+        "open_set_not_blind": True,
+        "summary": summary,
+        "verdicts": verdicts,
+        "rows": rows,
+        "errors": [row for row in rows if row.get("error")],
+    }
+    out = Path(args.json_out)
+    out.write_text(json.dumps(payload, ensure_ascii=False, indent=1, sort_keys=True) + "\n", encoding="utf-8")
+    print(json.dumps({"summary": summary, "verdicts": verdicts}, ensure_ascii=False, indent=1))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())