diff --git a/docs/BUG_HISTORY.md b/docs/BUG_HISTORY.md
index 37621259..1210ef2d 100644
--- a/docs/BUG_HISTORY.md
+++ b/docs/BUG_HISTORY.md
@@ -14705,18 +14705,19 @@
 
 ## BUG-1091 | 生时校正打分只奖不罚、权重未校准、高分辨率信号未计分
 
-- 状态：investigating（2026-09-29 研究单 `TASK-rectification-scoring-research-20260929` 脚手架已落地；判定按任务书须在 v5（≥60 例）上做，v4 上只跑通流水线、所有判定字段写 `pending_v5`）
-- 首次发现 / 最近更新：2026-09-29 / 2026-09-29
+- 状态：closed_by_design（2026-09-30 在 v5 77 例上判定：R-A 似然比校准权重与 R-B 缺席证据在三档半径 × truth/opposite 六格全部未过硬红线 1，打分层不立实现单；R-C 精度追问与同子集基线比无一致收益，同样不立实现单）
+- 首次发现 / 最近更新：2026-09-29 / 2026-09-30
 - 影响面：`scripts/active_rectification_event_engine.py::_score_event`（11 条加分规则，权重手拍）、`OBSERVATION_ONLY_LAYERS`（KP 宫头子主每分钟在算但不计分）、`_fine_minute_indices`（Pranapada / Hora / Ghati / Bhava lagna 已算未计分）、计分分盘无 D60。
 - 用户现象：开场说 12 件带年份经历、答约 10 张卡后范围整窗不动；引擎原始分头名簇命中在 v4 上只有 0.35 / 0.15 / 0.10（±10 / ±30 / ±60），约为随机水平的 4–5 倍。
 - 触发条件：任何一次校正。打分对候选「能解释事件」加分，对「预测了没发生的事」不扣分；一条规则对真分钟和错分钟同样常命中时照样 +2。
 - 根因（已确认）：(1) 只奖不罚——负证据只来自选择题答「没有」（`core/build-state.ts::applyProbeOutcome`，±2）；(2) 权重是人拍的，R1–R5 / V1 / V2 / V1n 都是「再拍一个权重看结果」，没有按数据校准；(3) KP / Pranapada / D60 这些分钟级信号没进计分，09-14 KP@默认权重更差是「权重没校准」的另一个症状。
-- 修复：未修。本轮只做离线脚手架：`scripts/research/scoring_research_lib.py`（特征记录 provider 与线上 `score_candidates` 逐分对账、似然比权重、leave-one-case-out、加权 provider、缺席 / 精度追问辅助）、`scripts/research/scoring_research.py`（R-A / R-B / R-C 流水线）、`tests/test_scoring_research.py`。生产代码与冻结文件零改动。
-- 验证：`tests/test_scoring_research.py`（含 v4 公开案例对账 0 差）；v4 全集流水线跑通、`PYTHONHASHSEED=0` 两次复跑 JSON 逐字节一致（见 `docs/tasks/PROGRESS-rectification-scoring-research-20260929.md`）。
-- 防复发：任何计分改动必须先在 v5 上过任务书硬红线（六格真值在区间不降、留一法数字、稳健性三项），再按 ERR-110 重冻结；不得在 v4 上下结论（BUG-1090）。
+- 修复：不修（研究不过门）。离线脚手架与判定：`scripts/research/scoring_research_lib.py`（特征记录 provider 与线上 `score_candidates` 逐分对账、似然比权重、leave-one-case-out、加权 provider、缺席 / 精度追问辅助）、`scripts/research/scoring_research.py`（R-A / R-B / R-C 流水线）、`tests/test_scoring_research.py`。生产代码与冻结文件零改动。
+- 验证：`tests/test_scoring_research.py`（含 v4 公开案例对账 0 差）；v4 与 v5 全集两次复跑 JSON 逐字节一致（`PYTHONHASHSEED=0`）；v5 对账 231/231 case-radius 差 0。
+- 判定数字（v5，`raw` 口径，留一法按案例；基线真值在区间 / 头名 = 76 / 0.675、76 / 0.494、75 / 0.325）：R-A A1 76 / 0.571、77 / 0.455、72 / 0.247；A2 75 / 0.545、76 / 0.416、72 / 0.247；A3 77 / 0.597、77 / 0.468、75 / 0.221——头名三档全降，六格不过。42 个特征里 36 / 38 / 39 个 bootstrap 区间跨 0，剩余 |log LR| ≤ 0.15，v4 与 v5 都稳定同号的特征 0 个。R-B @0.5 / 1.0 / 2.0 分：真值在区间 77 / 73 / 55、75 / 71 / 33、75 / 70 / 19，头名全降，六格不过。按方案矩阵重出六题与共用题目逐格相同（出题器不读矩阵分数）。全文 `docs/research/rectification_scoring_research_2026_09_29.md`。
+- 防复发：定论页 §9 记录「线上规则 + KP / Pranapada / D60 对分钟几乎无区分力、缺席年扣分伤真值」；再有人想从加权重 / 换尺度 / 缺席证据入手，先读 §9 与本条数字。任何计分改动仍须先在 v5 上过任务书硬红线，再按 ERR-110 重冻结。
 - 相关记录：BUG-560、BUG-1048、BUG-1089、BUG-1090
 - 复发自：无
-- 修复版本：未修（研究分支 `codex/rectification-scoring-research-20260929`）
+- 修复版本：未修（研究分支 `codex/rectification-scoring-research-20260929`；脚手架已合入 staging `84aaeadf`）
 
 ## BUG-1092 | 报告技术页标题出现「字段」，行星主星表头是看不懂的 RL / NL / SL / SS / SB
 
diff --git a/docs/research/rectification_minute_resolution_closure_2026_09_14.md b/docs/research/rectification_minute_resolution_closure_2026_09_14.md
index c663c6d5..bb08cbe3 100644
--- a/docs/research/rectification_minute_resolution_closure_2026_09_14.md
+++ b/docs/research/rectification_minute_resolution_closure_2026_09_14.md
@@ -105,3 +105,4 @@
 
 - §1.3「加权重类改法全部不过门」说的是**人拍权重**。2026-09-29 研究单换了路线：每条规则的权重由数据估（似然比，leave-one-case-out），允许负权重（只奖不罚是结构性问题），KP / Pranapada / D60 作为特征进入由数据定权重；另测「缺席证据」与「对已说事件追问月份」。
 - 脚手架与 v4 流水线见 `docs/research/rectification_scoring_research_2026_09_29.md`；**判定必须在 v5（≥60 例）上做**，v4 上的数字只是调试参考（BUG-1090 / BUG-1091）。本页 §1–§7 的结论不因此改变。
+- **2026-09-30 v5 判定（77 例，留一法按案例，`raw` 口径）：全部不过门。** R-A 三个方案（只奖 / 加 KP·Pranapada·D60 / 允许负权重）在 ±10 / ±30 / ±60 × truth/opposite 六格头名全降（±10 0.68 → 0.57–0.60，±60 0.32 → 0.22–0.25）；42 个特征里 36–39 个的 bootstrap 区间跨 0，其余 |log LR| ≤ 0.15，**v4 与 v5 同时稳定同号的特征为 0**。R-B 缺席年扣分按卡片强度把真值在区间从 76 / 76 / 75 压到 55 / 33 / 19，低强度不挤真值但头名全降。结论：**线上 11 条规则连同分钟级观察，对"哪一分钟"几乎没有信息；可校准的权重不存在。** §1.3 的结论由此从"人拍权重不过门"升级为"数据驱动权重也没有东西可校准"。BUG-560 维持 blocked，BUG-1091 closed_by_design。R-C（对已说的经历追问月份）与同子集基线比也没有一致收益（`rectification_scoring_research_2026_09_29.md` §4）。三条路都关闭。
diff --git a/docs/research/rectification_scoring_research_2026_09_29.md b/docs/research/rectification_scoring_research_2026_09_29.md
index e3648d75..0f672760 100644
--- a/docs/research/rectification_scoring_research_2026_09_29.md
+++ b/docs/research/rectification_scoring_research_2026_09_29.md
@@ -1,139 +1,167 @@
-# 生时校正打分方法研究：似然比权重 / 缺席证据 / 精度追问（脚手架阶段，2026-09-29）
+# 生时校正打分方法研究：似然比权重 / 缺席证据 / 精度追问（2026-09-29，v5 判定 2026-09-30）
 
 - 任务书：`docs/tasks/TASK-rectification-scoring-research-20260929.md`；进度：`docs/tasks/PROGRESS-rectification-scoring-research-20260929.md`
-- 分支：`codex/rectification-scoring-research-20260929`，基线 `origin/staging` @ `6e393780`（开工时 head；任务书写作时为 `5febb111`，其间只有文档提交）。
+- 分支：`codex/rectification-scoring-research-20260929`。脚手架阶段基线 `origin/staging` @ `6e393780`（已合入 staging，`84aaeadf`）；判定阶段基线 `origin/staging` @ `0120765f`（v5 已合入）。
 - 性质：**离线测量，不改线上代码、打分、阈值、出题闸门、Skill、冻结文件。** 研究计分器是 `precision_gate_lib.score_event_with_policy`（09-14 / 09-26 已证与线上逐分相同）加上不计分的"额外观察"，通过 `build_event_contribution_matrix(row_provider=…)` 走线上同一条矩阵 / 出题 / 六题回放链。
-- 数据：`references/real_case_calibration/minute_rectification_holdout_v4.json`，20 例公开 Rodden-AA。**这是开放集回放，不是盲测；本页所有数字都是 v4 调试参考，任务书规定判定只能在 v5（≥60 例）上做，所以每一项的判定栏都是 `pending_v5`。**
-- 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，题目由线上 G0 出题器按**线上矩阵**出一次、各方案共用）；交付区间 = 未淘汰且落后头名不足 8 分的簇并集。两种先验都报：`raw`（引擎行原始分，09-14 / 09-26 口径）与 `percent`（线上 `relative_support` 百分比口径；似然比方案用带温度的 softmax 后验，见 R-A）。
-- 复跑：`PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --cache-dir <临时目录>`（约 14 分钟）→ `docs/research/scoring_research_{lr,absence,precision_followup}_2026_09_29.json`。同机两次运行 JSON 逐字节一致（见进度记录）。
-- 判门：`precision_gate_lib.gate_verdict`（头名不降**且**宽度下降、覆盖不降）；JSON 里以 `debug_verdict_v4` 记录，`verdict` 字段一律 `pending_v5`。
+- 数据：判定用 `references/real_case_calibration/minute_rectification_holdout_v5.json`（77 例公开 Rodden-AA，964 件事件：day 244 / month 143 / year 577；v4 的 20 例原样继承）。v4（20 例）只用于脚手架调试，数字保留在 §6。**开放集回放，不是盲测；数字不是准确率。** v5 已知风险例（两例 LMT 时代记录先验即排除真值、一例 ±60 六题后挤出）按原样计入，未剔除。
+- 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 分的簇并集（`unionStillValidRange`）。两种先验都报：`raw`（引擎行原始分，09-14 / 09-26 / 09-29 口径）与 `percent`（线上 `relative_support` 百分比口径）。**主判口径 = `raw`**（理由见 §2.1）。
+- 复跑：`PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --holdout references/real_case_calibration/minute_rectification_holdout_v5.json --dataset-label v5 --cache-dir <临时目录> --probes-per-variant --out-suffix v5_2026_09_29`（约 48 分钟，静态 context 走磁盘缓存；一次只驻留一个半径）→ `docs/research/scoring_research_{lr,absence,precision_followup}_v5_2026_09_29.json`。同机两次运行三份 JSON `cmp` 逐字节一致（见进度记录）。
+- 判门：`precision_gate_lib.gate_verdict`（头名不降**且**宽度下降、覆盖不降）+ 任务书硬红线 1（每格真值在区间的例数不低于基线、头名不降）。
 
-## 结论
+## 1. 结论
 
-| 项 | 一句话结论（v4 调试参考） | 判定 |
+| 项 | 一句话结论（v5，77 例，留一法按案例） | 是否立实现单 |
 | --- | --- | --- |
-| R-0 脚手架 | 研究计分器与线上 `score_candidates` 在 20 例 × 3 档 = 60 个 case-radius 上**逐分对账 0 差**（分数与规则 id 都相同）；每个 (候选, 事件, 采样日期) 记录 42 个特征（线上规则 25 个 + 额外观察 17 个） | 完成 |
-| R-A 似然比权重 | 流水线跑通。v4 上：留一法 `raw` 口径头名 / 覆盖与基线持平或略动（±30 A3 头名 0.55 → 0.65、宽度 33 → 31），引擎自身头名（答题前）±10 0.30 → 0.40–0.45；`percent` 口径的 softmax 后验即使按训练集拟合温度仍过度自信，把真值挤出区间（±10 squeezed 3–5 例）。20 例上 42 个特征里 31–38 个的置信区间跨 0 | `pending_v5` |
-| R-B 缺席证据 | 流水线跑通。v4 上按卡片同等强度（2 分）扣分会大量挤出真值（±60 覆盖 1.00 → 0.40）——**v4 的事件表是传记摘录，不是完整时间线，空白年不等于没发生**；真人口述同样不完整。0.5 分档不挤真值但头名下降 | `pending_v5`（v4 信号明确为负，v5 上若不翻转即关闭） |
-| R-C 精度追问 | 流水线跑通。把日精度经历降成"只记得年"后，**每例平均只有 0.2 / 0.45 / 0.7 件经历在知道月份后能把候选分到两边**（±10 只有 2/20 例有）；对这些例子把那一件恢复到月精度重算先验（`C1_prior`）在 ±30 上 7 例头名 0.71 vs 该 7 例的 C0，其余档位样本太少 | `pending_v5` |
+| R-0 脚手架 | 研究计分器与线上 `score_candidates` 在 77 例 × 3 档 = 231 个 case-radius 上逐分对账 0 差；42 个特征（线上规则 25 + 额外观察 17） | 完成 |
+| R-A 似然比校准权重 | **不过门，三个方案六格全部未过硬红线 1。** `raw` 口径头名 ±10 0.68 → 0.57 / 0.55 / 0.60，±30 0.49 → 0.45 / 0.42 / 0.47，±60 0.32 → 0.25 / 0.25 / 0.22；真值在区间 A3 三档 77 / 77 / 75（基线 76 / 76 / 75）但头名全降，A1 / A2 在 ±60 各丢 3 例。**42 个特征里 36 / 38 / 39 个的置信区间跨 0，剩下的 \|log LR\| ≤ 0.15；v4 与 v5 都不跨 0 且同号的特征：0 个。** 数据说这些规则对"哪一分钟"几乎没有信息 | **不立**（BUG-1091 `closed_by_design`） |
+| R-B 缺席证据 | **不过门，六格全部未过。** 按卡片强度（2 分）扣把真值在区间压到 55 / 33 / 19（基线 76 / 76 / 75）；0.5 / 1.0 分不挤（77 / 75 / 75，73 / 71 / 70）但头名一律降、宽度不降。v5 每例平均 49 个缺席年——公开传记的空白年不是"没发生"，真人口述同样不完整 | **不立** |
+| R-C 精度追问 | **不过门。** 可追问的经历每例平均只有 0.44 / 0.71 / 1.09 件（有得问的例子 20 / 34 / 45）；对这些例子与同子集口述基线比，恢复到月精度重算先验只在 ±30 持平（头名 0.47 = 0.47、宽度 36 vs 37），±10 / ±60 头名降（0.70 vs 0.75、0.36 vs 0.42）；当作答"是"的卡三档头名都降。v4 上的正向信号在 77 例上消失 | **不立** |
 
-## R-0 · 脚手架与对账
+## 2. R-A · 似然比校准权重
 
-- `scripts/research/scoring_research_lib.py`
-  - `make_recording_provider`：对每个 legacy 单事件请求，用 `score_event_with_policy`（V0 基线）+ 线上的过运 / Ashtakavarga / Shadbala 辅助（取 context 里线上算好的结果）产出**与线上完全相同**的 evidence，同时把该候选在该事件下的额外观察写进 `FeatureStore`：
-    - 线上规则（记为特征）：`vim_{md,ad,pd}_domain_{house,lord,varga}`、`{label}_functional_{benefic,malefic}_auxiliary`、`narayana_{md,ad}_domain_house`、`{label}_arudha_auxiliary`、`controlled_transit_{jupiter,saturn}_domain_house`、`ashtakavarga_target_house_{support,pressure}_auxiliary`、`shadbala_sthana_drik_naisargika_{support,pressure}_auxiliary`。`event_kind:*`、`no_domain_activation`、`*_auxiliary_not_primary` 不作特征。
-    - 额外观察（线上算了没计分）：`kp_target_cusp_{sublord,starlord}_is_vim_{md,ad,pd}`、`kp_asc_sublord_is_vim_{md,ad,pd}`（`feature.kp_cusps.houses`，Placidus + Krishnamurti）、`vim_{md,ad,pd}_domain_varga_d60`（D60 现算）、`{pranapada,hora_lagna,ghati_lagna,bhava_lagna}_in_target_house`（`_fine_minute_indices`）。
-  - `reconcile_rows`：逐分比较分数与规则 id。**60/60 case-radius 差异 0。**
-  - `event_feature_matrix`：特征值 = 该规则在该事件采样日期上命中的比例（年精度 12 个采样日、月 3 个、日 1 个），与线上按采样日平均分数同口径。
-  - 真值标签：候选属于真值所在的**签名簇**（`raw_signature_clusters`）。
-- 测试：`tests/test_scoring_research.py`（10 条：LR 估计、留一法不泄漏、特征矩阵、规则过滤、加权 provider、缩放答案、缺席年、精度升降、百分比、以及 v4 公开案例 ±10 对账 0 差）。
+### 2.1 后验标定（缺口 2）与主判口径的选择
 
-## R-A · 似然比校准权重
+线上先验是 `relative_support` 的比例百分比；似然比方案的自然后验是 softmax(Σ log LR)。脚手架阶段（v4）发现 softmax 后验**过度自信**：即使按训练折拟合单温度（Platt，网格 0.25–256，最大化训练例真值簇的对数后验），仍有 15–25% 的例子被挤出区间。本轮的处理：
 
-**方法。** 对每个特征 f，`log LR_present = log P(f | 真值簇) − log P(f | 非真值簇)`，拉普拉斯平滑 α = 1（`(Σx + 1) / (N + 2)`）；`log LR_absent` 为朴素贝叶斯互补项。每档半径单独估。
+1. **主判口径用 `raw`**：研究分乘一个只从训练折估的 `scale`（研究分例内极差中位 → 线上极差中位），直接进 `unionStillValidRange`。理由：(a) 这是 09-14 / 09-26 / 09-29 三轮研究和定论页的口径，与既有数字可比；(b) ±2 / 8 分线的意义不变，判的是"先验换了之后六题还能不能把真值留在区间里"，不掺入后验标定这一层的好坏；(c) 不给"挑口径"留空间——口径在看到 v5 数字之前就写进了脚手架文档。
+2. **`percent` 口径照常报**（拟合温度的 softmax），用来回答"后验能不能标定"。v5 上拟合出的温度大得多（A1 16、A2 8–16、A3 4–16；v4 是 0.5–4），校准曲线在 ±30 / ±60 上几乎全部落在 [0,0.1) 桶（预测 0.03–0.06、实际 0.03–0.06），也就是说**标定之后后验接近均匀分布**——过自信解决了，代价是先验几乎不携带信息。这与 `raw` 口径的结论一致，不是口径差异。
+3. 两个口径下六格硬红线 1 都没过，所以口径选择不影响判定方向。
 
-| 方案 | 特征 | 权重规则 |
-| --- | --- | --- |
-| A1 | 只用线上 25 条规则 | 只奖不罚：`max(log LR_present, 0)`，不用 absent 项 |
-| A2 | 加 17 个额外观察 | 同上 |
-| A3 | 同 A2 | 朴素贝叶斯：present + absent 两项都用，允许负权重 |
+### 2.2 六格硬红线 1（真值在区间例数 ≥ 基线且头名不降；`raw` 口径，留一法）
 
-候选分 = Σ 事件 Σ 特征（x·w_present + (1−x)·w_absent），经线上矩阵的事件类型系数、精度权重、采样日平均与大运边界邻近项（后者按线上原样附加，不重加权）。两个只从**训练折**估的标定量：`scale`（研究分的例内极差中位 → 线上极差中位，让 `raw` 回放的 ±2 / 8 分线意义不变）、`temperature`（Platt 单温度，最大化训练例真值簇的对数后验；网格 0.25–256），`percent` 先验 = softmax(分 / T) × 100。**留一法按案例留**（`loo_folds`），全集拟合另报只作参照。
+D1（09-29 止血单，已上线）之后训练门开后不再注入口述 / 引导经历，truth 与 opposite 两个方向的格子是同一次回放；下表每行即一档的两格。
 
-### 六题回放（留一法；括号内为全集拟合）
+| 半径 | 基线 真值在区间 / 头名 | A1 | A2 | A3 |
+| --- | --- | --- | --- | --- |
+| ±10（truth = opposite） | 76 / 0.675 | 76 / 0.571 **不过** | 75 / 0.545 **不过** | 77 / 0.597 **不过**（头名降） |
+| ±30（truth = opposite） | 76 / 0.494 | 77 / 0.455 **不过** | 76 / 0.416 **不过** | 77 / 0.468 **不过** |
+| ±60（truth = opposite） | 75 / 0.325 | 72 / 0.247 **不过** | 72 / 0.247 **不过** | 75 / 0.221 **不过** |
 
-`raw` 口径：
+`percent` 口径：A1 76 / 77 / 77，A2 71 / 76 / 77，A3 66 / 75 / 76（基线 77 / 77 / 77）；头名全部低于基线 0.79 / 0.81 / 0.77。六格同样全部不过。
 
-| 半径 | 方案 | 头名命中 | 真值在区间 | 宽度中位 | 引擎头名（答题前） | 挤出真值 | debug 判定 |
+### 2.3 六题回放全表（留一法；括号内为全集拟合）
+
+| 半径 | 方案 | 头名命中 | 真值在区间 | 宽度中位 | 引擎头名（答题前） | 挤出 | gate_verdict |
 | --- | --- | ---: | ---: | ---: | ---: | ---: | --- |
-| ±10 | 基线 | 0.80 | 1.00 | 15 | 0.30 | 0 | — |
-| ±10 | A1 | 0.70 (0.75) | 1.00 | 14 | 0.45 | 0 | no_benefit |
-| ±10 | A2 | 0.75 (0.80) | 1.00 | 15 | 0.40 (0.50) | 0 | no_benefit |
-| ±10 | A3 | 0.80 (0.85) | 1.00 | 15 | 0.40 (0.50) | 0 | no_benefit |
-| ±30 | 基线 | 0.55 | 1.00 | 33 | 0.20 | 0 | — |
-| ±30 | A1 | 0.55 (0.70) | 1.00 | 32 | 0.25 | 0 | benefit（宽度 −1） |
-| ±30 | A2 | 0.60 (0.65) | 1.00 | 34 | 0.10 (0.30) | 0 | no_benefit |
-| ±30 | A3 | 0.65 (0.75) | 1.00 | 31 | 0.20 (0.45) | 0 | benefit（宽度 −2） |
-| ±60 | 基线 | 0.40 | 1.00 | 56 | 0.05 | 0 | — |
-| ±60 | A1 | 0.45 (0.50) | 1.00 | 61 | 0.15 (0.20) | 0 | no_benefit |
-| ±60 | A2 | 0.55 (0.60) | 1.00 | 61 | 0.00 (0.10) | 0 | no_benefit |
-| ±60 | A3 | 0.50 (0.70) | **0.95** | 61 | 0.05 (0.30) | 1 | no_benefit |
+| ±10 | 基线 | 0.675 | 0.987 | 15 | 0.169 | 1 | — |
+| ±10 | A1 | 0.571 (0.571) | 0.987 | 13 | 0.195 (0.182) | 1 | no_benefit |
+| ±10 | A2 | 0.545 (0.545) | 0.974 | 15 | 0.156 (0.169) | 2 | no_benefit |
+| ±10 | A3 | 0.597 (0.649) | 1.000 | 15 | 0.117 (0.182) | 0 | no_benefit |
+| ±30 | 基线 | 0.494 | 0.987 | 35 | 0.091 | 1 | — |
+| ±30 | A1 | 0.455 (0.532) | 1.000 | 35 | 0.052 (0.091) | 0 | no_benefit |
+| ±30 | A2 | 0.416 (0.494) | 0.987 | 35 | 0.065 (0.078) | 1 | no_benefit |
+| ±30 | A3 | 0.468 (0.494) | 1.000 | 35 | 0.078 (0.091) | 0 | no_benefit |
+| ±60 | 基线 | 0.325 | 0.974 | 63 | 0.052 | 2 | — |
+| ±60 | A1 | 0.247 (0.325) | 0.935 | 61 | 0.013 (0.078) | 5 | no_benefit |
+| ±60 | A2 | 0.247 (0.325) | 0.935 | 67 | 0.013 (0.026) | 5 | no_benefit |
+| ±60 | A3 | 0.221 (0.299) | 0.974 | 73 | 0.026 (0.052) | 2 | no_benefit |
 
-`percent` 口径（基线 = 线上比例百分比；A* = 拟合温度的 softmax 后验）：
+基线与扩集单发布的 v5 成绩单逐格一致（头名 0.68 / 0.49 / 0.32，真值在区间 76 / 76 / 75，宽度 15 / 35 / 63）。
 
-| 半径 | 方案 | 头名命中 | 真值在区间 | 宽度中位 | 挤出真值 | 温度（各折） |
-| --- | --- | ---: | ---: | ---: | ---: | --- |
-| ±10 | 基线 | 0.90 | 1.00 | 11 | 0 | — |
-| ±10 | A1 / A2 / A3 | 0.60 / 0.50 / 0.55 | 0.80 / 0.85 / 0.75 | 7 / 7 / 5 | 4 / 3 / 5 | 1–2 / 1 / 0.5–1 |
-| ±30 | 基线 | 0.85 | 1.00 | 33 | 0 | — |
-| ±30 | A1 / A2 / A3 | 0.65 / 0.55 / 0.55 | 0.95 / 0.80 / 0.90 | 31 / 10 / 13 | 1 / 4 / 2 | 2–4 / 1–2 / 1–2 |
-| ±60 | 基线 | 0.80 | 1.00 | 53 | 0 | — |
-| ±60 | A1 / A2 / A3 | 0.65 / 0.50 / 0.40 | 1.00 / 0.80 / 0.80 | 57 / 32 / 38 | 0 / 4 / 4 | 4–8 / 2–4 / 2 |
+- **缺口 3（按方案矩阵重出六题）已做**：`--probes-per-variant` 对每个留一法方案用它自己的矩阵重新出题再回放。结果与共用题目**逐格相同**（每档每方案头名 / 覆盖 / 宽度完全一致，每例题数 4.9 / 7.1 / 7.3）。原因：`discriminating_event_probes` 按代表候选与静态 context 判定分边，不读矩阵分数，所以先验换了题不换。这条缺口对判定方向无影响，可以关闭。
+- 稳健性（留一法，`raw`）：±7 天抖动真值在区间 A1 / A2 / A3 ±10 0.99 / 0.97 / 1.00，±30 1.00 / 0.99 / 1.00，±60 0.94 / 0.94 / 0.97；±1–3 月平移 0.99 / 0.99 / 1.00，0.99 / 0.99 / 1.00，0.94 / 0.95 / 1.00；答错 1 题 0.99 / 0.99 / 1.00，0.97 / 0.97 / 0.99，0.90 / 0.93 / 0.96；答错 2 题 0.94 / 0.95 / 0.98，0.91 / 0.89 / 0.94，0.82 / 0.87 / 0.86。与 09-26 基线（答错 2 题 ±60 0.90）同量级或更差，没有一项能补回头名的损失。
 
-**读法（v4，仅供 v5 前设计参考，不是结论）。**
+### 2.4 特征表：v4 与 v5 都稳定有信号的特征 —— 没有
 
-1. 对账通过后，`raw` 口径的留一法数字与基线基本持平：似然比权重没有让真值掉出区间（±60 A3 一例除外），也没有明显收窄；引擎答题前的头名在 ±10 从 0.30 到 0.40–0.45、±60 A1 从 0.05 到 0.15，全集拟合再高一些——**全集与留一法的差距就是 20 例过拟合的量**。
-2. `percent` 口径下 softmax 后验**过度自信**：即使温度按训练折拟合，仍把 15–25% 例子的真值挤出区间。校准曲线（A3 留一法）在 ±10 上 [0.20,0.30) 桶预测 0.24 / 实际 0.40、[0.50,0.70) 预测 0.58 / 实际 0.40、[0.70,1.00) 只有 1 个点且错；±30 / ±60 高桶几乎无样本。要上线必须先解决后验的标定，而不是先验本身。
-3. **42 个特征里 31 / 36 / 38 个（±10 / ±30 / ±60）的 5–95% 置信区间跨 0**（案例级 bootstrap 200 次）。±10 上区间不跨 0 的：`ashtakavarga_target_house_pressure_auxiliary`（+0.41）、`vim_ad_domain_varga`（+0.30）、`vim_md_domain_varga`（+0.27）、`ghati_lagna_in_target_house` / `hora_lagna_in_target_house`（+0.31）、`pranapada_in_target_house`（**−0.36**）、`vim_ad_arudha_auxiliary`（−0.21）、`narayana_ad_domain_house`（−0.21）、`kp_target_cusp_sublord_is_vim_ad`（−0.19）。方向与线上手拍权重相反的几条（Arudha、Narayana AD、KP AD 子主为负）正是 20 例上说不清的地方——这就是任务书要先扩集的原因。
-4. 稳健性（留一法 A3，`raw`）：±7 天抖动与 ±1–3 月平移三档真值在区间 0.95–1.00；答错 1 题 0.96–1.00、答错 2 题 0.91–0.99（与 09-26 基线同量级）。`percent` 口径下三项都更差（0.70–0.90），同样是后验过自信的表现。
+案例级 bootstrap 200 次的 5–95% 区间，v5 上**不跨 0** 的特征只剩：
 
-## R-B · 缺席证据
+| 半径 | 特征 | log LR（命中） | 区间 |
+| --- | --- | ---: | --- |
+| ±10 | `vim_md_domain_varga_d60` | −0.15 | [−0.27, −0.03] |
+| ±10 | `kp_asc_sublord_is_vim_ad` | −0.11 | [−0.22, −0.00] |
+| ±10 | `kp_target_cusp_starlord_is_vim_md` | +0.08 | [0.00, 0.16] |
+| ±10 | `kp_target_cusp_starlord_is_vim_ad` | +0.06 | [0.00, 0.13] |
+| ±10 / ±30 / ±60 | `shadbala_sthana_drik_naisargika_support_auxiliary` | −0.06 | [−0.10, −0.01] 三档 |
+| ±10 / ±30 / ±60 | `shadbala_sthana_drik_naisargika_pressure_auxiliary` | +0.05 | [0.01, 0.08] 三档 |
+| ±30 / ±60 | `vim_md_domain_varga_d60` | −0.15 / −0.14 | [−0.25, −0.05] / [−0.23, −0.04] |
+| ±30 | `kp_target_cusp_starlord_is_vim_md` | +0.09 | [0.00, 0.17] |
+
+其余 36 / 38 / 39 个跨 0。v4 上不跨 0 的九条（Ashtakavarga 压力、D-varga 命中、Hora / Ghati lagna、Pranapada 为负、Arudha 为负、Narayana AD 为负、KP AD 子主为负）在 v5 上**全部回到跨 0**；v5 上不跨 0 的几条在 v4 上也都跨 0。**没有一个特征在两套数据上同时稳定、同号。** 剩下的几条量级 0.05–0.15 nat（命中概率相差 1–2 个百分点），而且方向与线上手拍权重相反的居多（D60 命中、Shadbala 支持为负；Shadbala 压力为正）。
+
+**读法。** 这是本轮最硬的结果：线上 11 条规则加 17 个额外观察，对"真值分钟 vs 邻近分钟"的区分几乎为零；v4 上看到的"信号"是 20 例的噪声。让数据定权重（R-A）不是没做对，而是没有可校准的东西。定论页 §1.3"加权重类改法不过门"的结论被 77 例、留一法、数据驱动权重的方式再次确认，并且说明了原因。
+
+## 3. R-B · 缺席证据
 
 **方法。** 只用训练事件（留一件 holdout 不用）。每个领域取第一件与最后一件带年月事件之间的年份，没有该领域事件的年份 = 缺席年。对每个缺席 (领域, 年) 用出题器同一判定函数 `_evaluate_contexts(month=None)` 在代表候选上求"这一年该领域是否激活"的分边；分得开就当作一道答了"没有"的题，按 0.5 / 1.0 / 2.0 分扣（卡片是 2.0；低于卡片强度的扣分不计强冲突、不会淘汰）。之后照常六题。漏说稳健性：随机删掉 1–2 件训练事件再算缺席年（每例 5 次，种子固定）。
 
-`raw` 口径六题回放（`percent` 口径同向，见 JSON）：
+### 3.1 六格硬红线 1（`raw` 口径；truth = opposite）
 
-| 半径 | 方案 | 头名命中 | 真值在区间 | 宽度中位 | 挤出真值 | 漏说 1 件（100 次回放）真值在区间 | 漏说 2 件 |
-| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
-| ±10 | B0 | 0.80 | 1.00 | 15 | 0 | — | — |
-| ±10 | 缺席 @0.5 分 | 0.70 | 1.00 | 15 | 0 | 1.00 | 1.00 |
-| ±10 | 缺席 @1.0 分 | 0.70 | 1.00 | 14 | 0 | 1.00 | 1.00 |
-| ±10 | 缺席 @2.0 分（=卡片） | 0.55 | **0.85** | 13 | 3 | 0.94 | 0.94 |
-| ±30 | B0 | 0.55 | 1.00 | 33 | 0 | — | — |
-| ±30 | 缺席 @0.5 分 | 0.50 | 1.00 | 34 | 0 | 1.00 | 1.00 |
-| ±30 | 缺席 @1.0 分 | 0.45 | 1.00 | 33 | 0 | 1.00 | 1.00 |
-| ±30 | 缺席 @2.0 分 | 0.25 | **0.70** | 19 | 6 | 0.80 | 0.87 |
-| ±60 | B0 | 0.40 | 1.00 | 56 | 0 | — | — |
-| ±60 | 缺席 @0.5 分 | 0.35 | 1.00 | 68 | 0 | 1.00 | 1.00 |
-| ±60 | 缺席 @1.0 分 | 0.25 | 1.00 | 61 | 0 | 1.00 | 1.00 |
-| ±60 | 缺席 @2.0 分 | 0.10 | **0.40** | 9 | 12 | 0.54 | 0.73 |
+| 半径 | 基线 真值在区间 / 头名 | @0.5 分 | @1.0 分 | @2.0 分（=卡片） |
+| --- | --- | --- | --- | --- |
+| ±10 | 76 / 0.675 | 77 / 0.662 **不过**（头名降） | 73 / 0.649 **不过** | 55 / 0.481 **不过** |
+| ±30 | 76 / 0.494 | 75 / 0.442 **不过** | 71 / 0.429 **不过** | 33 / 0.169 **不过** |
+| ±60 | 75 / 0.325 | 75 / 0.221 **不过** | 70 / 0.195 **不过** | 19 / 0.091 **不过** |
 
-每例平均缺席年 17.95；能分边的缺席题 2.05 / 4.30 / 6.15 道（有题的例子 12 / 17 / 17）。三档 debug 判定全部 no_benefit。
+`percent` 口径同向（@2.0 分 54 / 33 / 20，@0.5 分头名 0.74 / 0.69 / 0.49 vs 基线 0.79 / 0.81 / 0.77）。
 
-读法：
+### 3.2 全表与漏说稳健性（`raw`）
 
-1. **按卡片强度扣，真值被大量挤出**（±60 覆盖 1.00 → 0.40）。原因不是方法错，而是 v4 的事件表是传记摘录：那些"空白年"里公众人物多半确有该领域的事，只是没进数据集；扣分等于用不完整的时间线否定真值。真人口述同样不完整（用户不会把每年的事都说全），所以这条在 v5 上大概率也是负的——**若 v5 上不翻转，R-B 关闭**。
-2. 0.5 / 1.0 分档不挤真值（覆盖 1.00）但头名一律下降、宽度不降，等于只加噪声。漏说 1–2 件在低强度下不伤覆盖，在卡片强度下把覆盖压到 0.54–0.94。
+| 半径 | 方案 | 头名 | 真值在区间 | 宽度中位 | 漏说 1 件（385 次）真值在区间 | 漏说 2 件 |
+| --- | --- | ---: | ---: | ---: | ---: | ---: |
+| ±10 | B0 | 0.675 | 0.987 | 15 | — | — |
+| ±10 | @0.5 | 0.662 | 1.000 | 15 | 1.000 | 0.997 |
+| ±10 | @1.0 | 0.649 | 0.948 | 15 | 0.953 | 0.953 |
+| ±10 | @2.0 | 0.481 | 0.714 | 9 | 0.751 | 0.753 |
+| ±30 | B0 | 0.494 | 0.987 | 35 | — | — |
+| ±30 | @0.5 | 0.442 | 0.974 | 35 | 0.974 | 0.974 |
+| ±30 | @1.0 | 0.429 | 0.922 | 33 | 0.932 | 0.945 |
+| ±30 | @2.0 | 0.169 | 0.429 | 5 | 0.465 | 0.488 |
+| ±60 | B0 | 0.325 | 0.974 | 63 | — | — |
+| ±60 | @0.5 | 0.221 | 0.974 | 71 | 0.974 | 0.969 |
+| ±60 | @1.0 | 0.195 | 0.909 | 57 | 0.917 | 0.933 |
+| ±60 | @2.0 | 0.091 | 0.247 | 5 | 0.301 | 0.369 |
 
-## R-C · 精度追问可行性
+每例平均缺席年 48.9；能分边的缺席题 3.9 / 7.6 / 10.8 道（有题的例子 60 / 72 / 73）。
 
-**方法。** 把每例所有日精度经历降成"只记得年"（v4 没有月精度）作为口述形态 `C0_spoken`，先验按线上重算。对每件训练中的日精度经历，用 `_evaluate_contexts(month=真实月份)` 判断"知道月份后能否把代表候选分到两边"；能分的按信息增益排序取前 1 / 2 件：`C{k}_prior`（那件恢复到月精度重算先验）、`C{k}_probe`（当作答"是"的卡）、`C{k}_both`。
+**读法。** 缺席年扣分在三档、三档强度、两种口径下没有一格过门。卡片强度下区间是窄了（5–9 分钟）但真值多半不在里面——这正是定论页 §6 第 3 条警告的"把区间做窄很容易"。原因是"时间线上没有"不等于"没发生"：公开传记如此，真人口述更如此。**关闭。**
+
+## 4. R-C · 精度追问可行性
+
+**方法。** 把每例所有日 / 月精度经历降成"只记得年"作为口述形态 `C0_spoken`，先验按线上重算。对每件训练中的日 / 月精度经历，用 `_evaluate_contexts(month=真实月份)` 判断"知道月份后能否把代表候选分到两边"；能分的按信息增益排序取前 1 / 2 件：`C{k}_prior`（那件恢复到月精度重算先验，走线上已有的打字经历通道）、`C{k}_probe`（当作答"是"的卡）、`C{k}_both`。**方案臂只覆盖"有得问"的子集，所以与同一子集上的 `C0`（`subset_C0`）比**，不与全集比。
 
 | 半径 | 每例可追问件数均值 | ≥1 件的例子 | ≥2 件的例子 |
 | --- | ---: | ---: | ---: |
-| ±10 | 0.20 | 2 / 20 | 1 / 20 |
-| ±30 | 0.45 | 7 / 20 | 1 / 20 |
-| ±60 | 0.70 | 10 / 20 | 3 / 20 |
+| ±10 | 0.44 | 20 / 77 | 10 / 77 |
+| ±30 | 0.71 | 34 / 77 | 12 / 77 |
+| ±60 | 1.09 | 45 / 77 | 23 / 77 |
 
-| 半径 | 方案 | n | 头名命中（raw） | 真值在区间 | 宽度中位 | 同 n 例的 C0（raw 头名 / 宽度） |
-| --- | --- | ---: | ---: | ---: | ---: | --- |
-| ±10 | C1_prior / C1_probe | 2 | 1.00 / 0.50 | 1.00 | 16 | 见 JSON per-arm（样本太少） |
-| ±30 | C1_prior | 7 | 0.71 | 1.00 | 31 | debug 判定 benefit（percent 口径头名 1.00） |
-| ±30 | C1_probe | 7 | 0.57 | 1.00 | 33 | no_benefit |
-| ±60 | C1_prior / C1_probe | 10 | 0.40 / 0.40 | 1.00 | 58 / 63 | no_benefit |
+六格硬红线 1 与同子集 C0 比（`raw`；truth = opposite）：
 
-**读法。** 可追问的经历很少（与 09-29 打字经历研究一致：绝大多数经历落在所有候选都一样的大运段），但在 ±30 上"恢复到月精度重算先验"（走线上已有的打字经历通道，不加卡片）对那 7 例有正向信号；"当作答是的卡"没有。样本太少，只能说值得在 v5 上按同口径重测；产品层实现（只对落在候选分歧点的那一件追问月份，算在定向追问 2 条额度内）要等 v5 判定。
+| 半径 | 方案 | n | 真值在区间（方案 / 子集 C0） | 头名（方案 / 子集 C0） | 宽度中位（方案 / 子集 C0） | 硬红线 1 | gate_verdict |
+| --- | --- | ---: | --- | --- | --- | --- | --- |
+| ±10 | C1_prior | 20 | 20 / 20 | 0.70 / 0.75 | 13 / 13 | **不过** | no_benefit |
+| ±10 | C1_probe | 20 | 20 / 20 | 0.60 / 0.75 | 12 / 13 | **不过** | no_benefit |
+| ±10 | C2_prior | 10 | 10 / 10 | 0.70 / 0.80 | 11 / 11 | **不过** | no_benefit |
+| ±30 | C1_prior | 34 | 34 / 34 | 0.47 / 0.47 | 36 / 37 | 过（头名持平，宽度 −1） | benefit |
+| ±30 | C1_probe | 34 | 34 / 34 | 0.41 / 0.47 | 32 / 37 | **不过** | no_benefit |
+| ±30 | C2_prior | 12 | 12 / 12 | 0.42 / 0.50 | 31 / 36 | **不过** | no_benefit |
+| ±60 | C1_prior | 45 | 44 / 44 | 0.36 / 0.42 | 71 / 71 | **不过** | no_benefit |
+| ±60 | C1_probe | 45 | 44 / 44 | 0.38 / 0.42 | 63 / 71 | **不过** | no_benefit |
+| ±60 | C2_prior | 23 | 22 / 22 | 0.48 / 0.52 | 75 / 75 | **不过** | no_benefit |
 
-## 给产品的话
+`percent` 口径：`C1_prior` ±30 头名 0.79 vs 0.71、宽度 35 vs 32（宽度变宽，gate no_benefit）、±10 0.80 vs 0.85、±60 0.76 vs 0.80；`C1_probe` ±30 0.74 vs 0.71 且宽度 31 vs 32（gate benefit）但 ±10 / ±60 头名降。两个口径下没有一个臂在三档同时过。
 
-- 三条流水线都跑通、与线上对账 0 差，**但没有任何一条在 v4 上能下结论**，20 例上 42 个特征有四分之三分不清正负。这一轮交付的是"能在 v5 上一键复跑的测量工具"，不是新打分。
-- v4 上唯一方向明确的信号是负的：把口述时间线的空白当"没发生"会伤真值（R-B）。
-- v5 交付后的复跑命令：`PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --holdout references/real_case_calibration/minute_rectification_holdout_v5.json --dataset-label v5 --out-suffix v5_<日期>`。
+**读法。** (1) 能追问的经历确实少：±10 上 77 例只有 20 例有一件"知道月份就能分开候选"的经历，与 09-29 打字经历研究一致。(2) 对这些例子，"恢复到月精度重算先验"（走现有通道）在 ±30 上持平、±10 / ±60 上头名反而降；"当作答是的卡"在 ±10 / ±60 也降。之前 v4 上 ±30 的 7 例正向信号（0.71）在 77 例、同子集比较下消失。(3) 结论：**精度追问没有一致收益，不立实现单。** 产品若仍想做"只追问一件落在候选分歧点的经历"，那是交互层面的取舍，本研究给不出精度上的理由。
+
+## 5. 给产品的话
+
+- **打分层这条路可以收口了。** 77 例、留一法、让数据定权重、加进 KP / Pranapada / D60，结果是：线上规则对"哪一分钟"的区分几乎为零，没有可校准的权重；把口述时间线的空白当"没发生"会伤真值。BUG-560（换尺度）继续 blocked、BUG-1091 按设计关闭。收窄仍然只能靠带年月的选择题——这与 09-14 定论一致，现在有了 77 例的证据。
+- 产品层的"对已说的事追问月份"（§4）与同子集基线比也没有一致收益：能追问的例子少（±10 只有 20 / 77），追问后头名多半不升。若要做，是交互层面的决定，不是精度收益。
+- 复跑：本页顶部命令；判定所用 JSON `dataset: v5`。
+
+## 6. v4 脚手架阶段的数字（20 例，仅调试参考）
+
+- 对账：20 例 × 3 档 = 60 个 case-radius 差异 0；两次复跑 JSON 逐字节一致（`scoring_research_*_2026_09_29.json`）。
+- R-A `raw` 留一法头名：±10 A1 / A2 / A3 0.70 / 0.75 / 0.80（基线 0.80）；±30 0.55 / 0.60 / 0.65（0.55）；±60 0.45 / 0.55 / 0.50（0.40）。真值在区间除 ±60 A3 0.95 外全 1.00。`percent` 口径挤出 3–5 例。42 个特征里 31 / 36 / 38 个跨 0。
+- R-B：@2.0 分覆盖 0.85 / 0.70 / 0.40；@0.5 / 1.0 覆盖 1.00 但头名降。
+- R-C：可追问件数每例均值 0.20 / 0.45 / 0.70；±30 C1_prior 7 例头名 0.71。
+- 这些数字与 v5 方向一致，但 v4 上看似"有信号"的九个特征在 v5 上全部回到噪声——扩集单（BUG-1090）的必要性由此坐实。
 
 ## 附：复跑命令
 
 ```bash
-PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --cache-dir /tmp/scoring_ctx        # 约 14 分钟，写三份 JSON
-PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --limit 3 --radii 10 --fast --no-write   # 开发用
+PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --holdout references/real_case_calibration/minute_rectification_holdout_v5.json --dataset-label v5 --cache-dir /tmp/scoring_ctx --probes-per-variant --out-suffix v5_2026_09_29   # 约 48 分钟
+PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --cache-dir /tmp/scoring_ctx                       # v4，约 13 分钟
+PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --limit 3 --radii 10 --fast --no-write            # 开发用
 python3 -m pytest tests/test_scoring_research.py -q
 ```
diff --git a/docs/research/scoring_research_absence_v5_2026_09_29.json b/docs/research/scoring_research_absence_v5_2026_09_29.json
new file mode 100644
index 00000000..65f264e5
--- /dev/null
+++ b/docs/research/scoring_research_absence_v5_2026_09_29.json
@@ -0,0 +1,2619 @@
+{
+ "ask_count": 6,
+ "ayanamsa": "raman",
+ "card_points": 2.0,
+ "case_count": 77,
+ "dataset": "v5",
+ "errors": [],
+ "fast": false,
+ "generated_for": "TASK-rectification-scoring-research-20260929",
+ "holdout": "references/real_case_calibration/minute_rectification_holdout_v5.json",
+ "minute_step": 2,
+ "node_mode": "mean",
+ "omit_repeats": 5,
+ "open_set_not_blind": true,
+ "part": "R-B",
+ "points": [
+  0.5,
+  1.0,
+  2.0
+ ],
+ "probe_today": "2026-09-16",
+ "probes": "production G0 probes generated once per case/radius from the production matrix; reused for every arm (own_probes = regenerated from the variant matrix)",
+ "radii": {
+  "10": {
+   "arms": {
+    "B0": {
+     "percent": {
+      "coverage": 1.0,
+      "engine_top1": 0.1688,
+      "entropy": 2.0559,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 0,
+      "tie": 0.013,
+      "top1": 0.7922,
+      "truth_eliminated": 0,
+      "width_median": 13.0
+     },
+     "raw": {
+      "coverage": 0.987,
+      "engine_top1": 0.1688,
+      "entropy": 2.0586,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 1,
+      "tie": 0.0,
+      "top1": 0.6753,
+      "truth_eliminated": 0,
+      "width_median": 15.0
+     }
+    },
+    "absence@0.5": {
+     "gate_verdict": {
+      "percent": "no_benefit",
+      "raw": "no_benefit"
+     },
+     "hard_line_1": {
+      "percent": {
+       "baseline_coverage_count": 77,
+       "coverage_count": 77,
+       "pass": false
+      },
+      "raw": {
+       "baseline_coverage_count": 76,
+       "coverage_count": 77,
+       "pass": false
+      }
+     },
+     "percent": {
+      "coverage": 1.0,
+      "engine_top1": 0.1688,
+      "entropy": 2.0556,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 0,
+      "tie": 0.0,
+      "top1": 0.7403,
+      "truth_eliminated": 0,
+      "width_median": 15.0
+     },
+     "raw": {
+      "coverage": 1.0,
+      "engine_top1": 0.1688,
+      "entropy": 2.059,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 0,
+      "tie": 0.0,
+      "top1": 0.6623,
+      "truth_eliminated": 0,
+      "width_median": 15.0
+     },
+     "verdict": "pending_v5"
+    },
+    "absence@1.0": {
+     "gate_verdict": {
+      "percent": "no_benefit",
+      "raw": "no_benefit"
+     },
+     "hard_line_1": {
+      "percent": {
+       "baseline_coverage_count": 77,
+       "coverage_count": 73,
+       "pass": false
+      },
+      "raw": {
+       "baseline_coverage_count": 76,
+       "coverage_count": 73,
+       "pass": false
+      }
+     },
+     "percent": {
+      "coverage": 0.9481,
+      "engine_top1": 0.1688,
+      "entropy": 2.0476,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 4,
+      "tie": 0.0,
+      "top1": 0.7143,
+      "truth_eliminated": 0,
+      "width_median": 15.0
+     },
+     "raw": {
+      "coverage": 0.9481,
+      "engine_top1": 0.1688,
+      "entropy": 2.0544,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 4,
+      "tie": 0.0,
+      "top1": 0.6494,
+      "truth_eliminated": 0,
+      "width_median": 15.0
+     },
+     "verdict": "pending_v5"
+    },
+    "absence@2.0": {
+     "gate_verdict": {
+      "percent": "no_benefit",
+      "raw": "no_benefit"
+     },
+     "hard_line_1": {
+      "percent": {
+       "baseline_coverage_count": 77,
+       "coverage_count": 54,
+       "pass": false
+      },
+      "raw": {
+       "baseline_coverage_count": 76,
+       "coverage_count": 55,
+       "pass": false
+      }
+     },
+     "percent": {
+      "coverage": 0.7013,
+      "engine_top1": 0.1688,
+      "entropy": 1.1996,
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+      "refresh_mean": 0.0,
+      "squeezed": 23,
+      "tie": 0.039,
+      "top1": 0.5325,
+      "truth_eliminated": 24,
+      "width_median": 9.0
+     },
+     "raw": {
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+      "entropy": 1.2004,
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+      "refresh_mean": 0.0,
+      "squeezed": 22,
+      "tie": 0.0,
+      "top1": 0.4805,
+      "truth_eliminated": 24,
+      "width_median": 9.0
+     },
+     "verdict": "pending_v5"
+    }
+   },
+   "gap_stats": {
+    "cases_with_probes": 60,
+    "mean_absence_probes": 3.92,
+    "mean_absent_years": 48.88
+   },
+   "n": 77,
+   "omission": {
+    "omit_1@0.5": {
+     "percent": {
+      "coverage": 1.0,
+      "engine_top1": 0.1688,
+      "entropy": 2.0556,
+      "n": 385,
+      "refresh_mean": 0.0,
+      "squeezed": 0,
+      "tie": 0.0104,
+      "top1": 0.7662,
+      "truth_eliminated": 0,
+      "width_median": 15.0
+     },
+     "raw": {
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+      "engine_top1": 0.1688,
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+      "refresh_mean": 0.0,
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+      "tie": 0.0,
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+      "truth_eliminated": 0,
+      "width_median": 15.0
+     }
+    },
+    "omit_1@1.0": {
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+      "entropy": 2.0485,
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+      "refresh_mean": 0.0,
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+      "tie": 0.0,
+      "top1": 0.7221,
+      "truth_eliminated": 0,
+      "width_median": 15.0
+     },
+     "raw": {
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+      "refresh_mean": 0.0,
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+      "tie": 0.0,
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+      "truth_eliminated": 0,
+      "width_median": 15.0
+     }
+    },
+    "omit_1@2.0": {
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+      "width_median": 9.0
+     },
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+      "truth_eliminated": 105,
+      "width_median": 9.0
+     }
+    },
+    "omit_2@0.5": {
+     "percent": {
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+      "tie": 0.0104,
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+      "width_median": 15.0
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+      "width_median": 15.0
+     }
+    },
+    "omit_2@1.0": {
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+      "tie": 0.0,
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+      "truth_eliminated": 0,
+      "width_median": 13.0
+     },
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+      "width_median": 15.0
+     }
+    },
+    "omit_2@2.0": {
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+      "engine_top1": 0.1688,
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+      "refresh_mean": 0.0,
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+      "tie": 0.0416,
+      "top1": 0.5714,
+      "truth_eliminated": 102,
+      "width_median": 9.0
+     },
+     "raw": {
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+      "engine_top1": 0.1688,
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+      "refresh_mean": 0.0,
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+      "tie": 0.0,
+      "top1": 0.5065,
+      "truth_eliminated": 102,
+      "width_median": 9.0
+     }
+    }
+   }
+  },
+  "30": {
+   "arms": {
+    "B0": {
+     "percent": {
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+      "engine_top1": 0.0909,
+      "entropy": 3.2757,
+      "n": 77,
+      "refresh_mean": 0.0,
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+      "tie": 0.0,
+      "top1": 0.8052,
+      "truth_eliminated": 0,
+      "width_median": 35.0
+     },
+     "raw": {
+      "coverage": 0.987,
+      "engine_top1": 0.0909,
+      "entropy": 3.3002,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 1,
+      "tie": 0.0,
+      "top1": 0.4935,
+      "truth_eliminated": 0,
+      "width_median": 35.0
+     }
+    },
+    "absence@0.5": {
+     "gate_verdict": {
+      "percent": "no_benefit",
+      "raw": "no_benefit"
+     },
+     "hard_line_1": {
+      "percent": {
+       "baseline_coverage_count": 77,
+       "coverage_count": 76,
+       "pass": false
+      },
+      "raw": {
+       "baseline_coverage_count": 76,
+       "coverage_count": 75,
+       "pass": false
+      }
+     },
+     "percent": {
+      "coverage": 0.987,
+      "engine_top1": 0.0909,
+      "entropy": 3.2337,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 1,
+      "tie": 0.0,
+      "top1": 0.6883,
+      "truth_eliminated": 0,
+      "width_median": 35.0
+     },
+     "raw": {
+      "coverage": 0.974,
+      "engine_top1": 0.0909,
+      "entropy": 3.2978,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 2,
+      "tie": 0.0,
+      "top1": 0.4416,
+      "truth_eliminated": 0,
+      "width_median": 35.0
+     },
+     "verdict": "pending_v5"
+    },
+    "absence@1.0": {
+     "gate_verdict": {
+      "percent": "no_benefit",
+      "raw": "no_benefit"
+     },
+     "hard_line_1": {
+      "percent": {
+       "baseline_coverage_count": 77,
+       "coverage_count": 72,
+       "pass": false
+      },
+      "raw": {
+       "baseline_coverage_count": 76,
+       "coverage_count": 71,
+       "pass": false
+      }
+     },
+     "percent": {
+      "coverage": 0.9351,
+      "engine_top1": 0.0909,
+      "entropy": 3.0817,
+      "n": 77,
+      "refresh_mean": 0.0,
+      "squeezed": 5,
+      "tie": 0.0,
+      "top1": 0.5844,
+      "truth_eliminated": 0,
+      "width_median": 33.0
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diff --git a/docs/research/scoring_research_lr_v5_2026_09_29.json b/docs/research/scoring_research_lr_v5_2026_09_29.json
new file mode 100644
index 00000000..4dbc9c75
--- /dev/null
+++ b/docs/research/scoring_research_lr_v5_2026_09_29.json
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diff --git a/docs/research/scoring_research_precision_followup_v5_2026_09_29.json b/docs/research/scoring_research_precision_followup_v5_2026_09_29.json
new file mode 100644
index 00000000..00121095
--- /dev/null
+++ b/docs/research/scoring_research_precision_followup_v5_2026_09_29.json
@@ -0,0 +1,17096 @@
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+   {
+    "candidates": 61,
+    "case_id": "rafael_nadal_1986_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "rudy_fernandez_1952_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "salvador_dali_1904_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "serena_williams_1981_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "silvio_santos_1930_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "sophia_loren_1934_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "venus_williams_1980_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "vincent_van_gogh_1853_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "whitney_houston_1963_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   },
+   {
+    "candidates": 61,
+    "case_id": "zinedine_zidane_1972_aa_v5_holdout",
+    "changed": 0,
+    "details": [],
+    "radius": 60
+   }
+  ],
+  "unreconciled": 0
+ },
+ "seed": 20260929,
+ "verdict": "see_report"
+}
diff --git a/docs/tasks/PROGRESS-rectification-scoring-research-20260929.md b/docs/tasks/PROGRESS-rectification-scoring-research-20260929.md
index 7660e524..325b72f3 100644
--- a/docs/tasks/PROGRESS-rectification-scoring-research-20260929.md
+++ b/docs/tasks/PROGRESS-rectification-scoring-research-20260929.md
@@ -1,73 +1,67 @@
-# PROGRESS · 生时校正打分方法研究：似然比权重 / 缺席证据 / 精度追问（脚手架阶段，2026-09-29）
+# PROGRESS · 生时校正打分方法研究：似然比权重 / 缺席证据 / 精度追问（2026-09-29 脚手架，2026-09-30 v5 判定）
 
 - 任务书：`docs/tasks/TASK-rectification-scoring-research-20260929.md`
-- 执行：Claude（直接执行模式，fork 子代理），分支 `codex/rectification-scoring-research-20260929`，基线 `origin/staging` @ `6e393780`（任务书写作时 `5febb111`，其间只有文档提交）。**未推送。**
-- 研究报告：`docs/research/rectification_scoring_research_2026_09_29.md` + `scoring_research_{lr,absence,precision_followup}_2026_09_29.json`
-- 本轮范围（按调度方指令）：在 v4 上完成 R-0 脚手架并把 R-A / R-B / R-C 流水线跑通；**所有判定字段写 `pending_v5`**，v4 数字只作调试参考。v5 交付后在 v5 上跑判定（复跑命令见研究报告末尾）。
+- 执行：Claude（直接执行模式，fork 子代理），分支 `codex/rectification-scoring-research-20260929`。脚手架阶段基线 `origin/staging` @ `6e393780`，已合入 staging（`84aaeadf`）；判定阶段 `git rebase origin/staging` 到 `0120765f`（v5 已合入，分支无额外提交后再开新提交）。**未推送。**
+- 研究报告：`docs/research/rectification_scoring_research_2026_09_29.md` + `scoring_research_{lr,absence,precision_followup}_2026_09_29.json`（v4）与 `…_v5_2026_09_29.json`（v5，判定用）。
 
-## 开工
+## 阶段一（2026-09-29）· 脚手架与 v4 流水线
 
-- `git fetch origin --prune` → worktree `.worktrees/rectification-scoring-research-20260929` 基于 `origin/staging`；任何 git 操作前 `git status -sb` 核对分支。
-- 先读：定论页（含 §7 勘误）、`rectification_offline_research_2026_09_26.md`、`rectification_typed_event_scoring_2026_09_29.md`、ERR-110 / ERR-111。
-- 环境：本机没有 `.venv`（所有 worktree 的 `.venv` 软链都指向不存在的 `/workspace/Jyotisha/.venv`），全部用系统 `python3` 3.13.5（swisseph 9.1.1、pytest、timezonefinder 齐）。快速门的前端步需要 Node 22：`frontend/node_modules` 软链到主检出，`npm` 用 `/exec-daemon/node` + `/exec-daemon/lib/node_modules/npm` 的 shim（系统 `node` 是 20.19，`/usr/bin/npm` 跑的 `tsx` 不在 PATH）。`PYTHONHASHSEED=0`（ERR-111）。
-- 另一子代理并行做 v5 扩集（`.worktrees/rectification-holdout-expansion-20260929`），未触碰。
+- 环境：本机没有 `.venv`，全部用系统 `python3` 3.13.5（swisseph 9.1.1）。快速门的前端步需要 Node 22：`frontend/node_modules` 软链到主检出（未入库），`npm` 用 `/exec-daemon/node` + `/exec-daemon/lib/node_modules/npm` 的 shim。`PYTHONHASHSEED=0`（ERR-111）。
+- 交付：`scripts/research/scoring_research_lib.py`（特征记录 provider、逐分对账、似然比估计 α = 1、案例级 bootstrap、加权 provider、leave-one-case-out、缩放答案、Platt 温度、缺席年 / 精度升降辅助）、`scripts/research/scoring_research.py`（R-A / R-B / R-C 流水线）、`tests/test_scoring_research.py`（10 条，含 v4 公开案例 ±10 对账 0 差）。
+- 对账：20 例 × 3 档 = 60 个 case-radius 分数差 0、规则 id 差 0。两次复跑 JSON 逐字节一致。所有判定字段 `pending_v5`。
+- 生产代码、`sealed_holdout_rerun.py::PRODUCTION_FILES` 的 10 个冻结文件、任何常数零改动。
+- 已由 Claude 验收合入 staging（README 状态板记录）。
 
-## 交付物
+## 阶段二（2026-09-30）· v5 判定
 
-| 文件 | 内容 |
-| --- | --- |
-| `scripts/research/scoring_research_lib.py` | R-0：特征记录 provider（线上同源 evidence + 额外观察）、逐分对账、特征矩阵、似然比估计（α = 1）、案例级 bootstrap、加权 provider、leave-one-case-out、缩放答案、Platt 温度、缺席年 / 精度升降 / 月份平移辅助 |
-| `scripts/research/scoring_research.py` | R-A / R-B / R-C 流水线；`--limit / --radii / --parts / --fast / --cache-dir / --holdout / --dataset-label / --out-suffix` |
-| `tests/test_scoring_research.py` | 10 条：纯函数 9 条 + v4 公开案例 ±10 对账 0 差 1 条 |
-| `docs/research/rectification_scoring_research_2026_09_29.md` | 报告（结构按任务书 R-D；结论栏全部 `pending_v5`；复跑命令） |
-| `docs/research/scoring_research_*_2026_09_29.json` | 三份结果（`dataset: v4, verdict: pending_v5`） |
-| `docs/BUG_HISTORY.md` | BUG-1091 `investigating`（开工核对最大号 1089；1090 由扩集单占用，未冲突） |
-| `docs/research/ACTIVE_FRONTS.md`、定论页 §9、`docs/tasks/README.md` | 索引与状态板 |
+### 脚本改动（本次提交，`scripts/research/scoring_research.py`）
 
-生产代码、`sealed_holdout_rerun.py::PRODUCTION_FILES` 的 10 个冻结文件、任何常数：**零改动**（`git status` 只有上表文件）。
+1. `main` 改为**一次只驻留一个半径**：77 例 × 3 档的静态 context 同时驻留会 OOM（两次被系统 kill，anon-rss 3.2 GB），按半径加载 → 跑 R-A / R-B / R-C → 释放。JSON 结构不变。
+2. `fit_prior` 的训练折计分改用线性代理 `CaseData.proxy_rows`（特征权重 × 精度权重，不含事件类型系数 / 大运边界邻近项），把每折 O(n) 次矩阵构建降为字典求和；留出例的分数仍走线上矩阵。
+3. `--probes-per-variant`：留一法方案用自己的矩阵重新出六题再回放（缺口 3）。
+4. `hard_line_1` 字段：每格真值在区间例数与头名 vs 基线；R-C 增加同一子集的 `subset_C0` / `subset_B0` 与 `gate_verdict_vs_subset_C0`（子集方案对全集 C0 比是不公平的，改为同子集比）。
+5. `per_case` 行（R-A 各方案与基线、R-C 各臂）写进 JSON，供验收逐例核对。
+6. `verdict` 字段：v4 仍 `pending_v5`；v5 文件 header 写 `see_report`，方案级 `gate_verdict` / `hard_line_1.pass` 是机器判定，正式判定见研究报告 §1。
 
-## R-0 · 对账（任务书硬红线 4）
+### 对账与复跑
 
-- 研究计分器 vs 线上 `score_candidates`（经 `build_event_contribution_matrix` 同一条矩阵链）：**20 例 × 3 档 = 60 个 case-radius，分数差 0、规则 id 差 0**（`reconciliation.unreconciled = 0`，三份 JSON 均记录）。
-- 做法：provider 用 `precision_gate_lib.score_event_with_policy`（V0）+ 线上 `_controlled_transit_rules`（带缓存）/ `_ashtakavarga_auxiliary` / `_shadbala_verified_components_auxiliary`（取 context 里线上算好的结果），返回与 `_candidate_row` 相同的 evidence；额外观察只进 `FeatureStore`，不进 rule_ids / points，所以矩阵、`event_kind` 系数、`technique_layers`、出题都与线上一致。
-- 特征 42 个：线上规则 25 + 额外观察 17（KP 宫头子主 / 星主 × MD/AD/PD、KP 上升子主、D60 × MD/AD/PD、Pranapada / Hora / Ghati / Bhava lagna 落领域宫）。特征值 = 该规则在该事件采样日期上命中的比例。真值标签 = 候选属于真值所在签名簇。
-- 留一法按**案例**留（`loo_folds`），`scale` 与 `temperature` 也只从训练折估。
+- v5 对账：**77 例 × 3 档 = 231 个 case-radius 差异 0**（`reconciliation.unreconciled = 0`，三份 JSON 均记录）。
+- 复跑：`PYTHONHASHSEED=0 … --dataset-label v5 --probes-per-variant` 连跑两次（2918 s / 2818 s），三份 JSON `cmp` **逐字节一致**（`lr` 407,129 B、`absence` 53,227 B、`precision_followup` 48,521 B）；R-C 加同子集基线后单独再跑两次（`--parts rc`），同样逐字节一致；`_v5_rerun` 副本比对后删除，不入库。
+- 基线自检：本脚本的 v5 基线（六题后 `raw`）头名 0.675 / 0.494 / 0.325、真值在区间 76 / 76 / 75、宽度 15 / 35 / 63，与扩集单发布的 v5 成绩单逐格一致。
 
-## R-A / R-B / R-C · 流水线状态
+### 判定（`raw` 主判口径，理由见报告 §2.1；truth = opposite，D1 之后六题后不再注入）
 
-| 项 | 状态 | v4 调试参考（`raw` 口径，留一法） | 判定 |
+| 项 | 六格硬红线 1（真值在区间例数 / 头名 vs 基线 76 / 0.675、76 / 0.494、75 / 0.325） | `gate_verdict` | 判定 |
 | --- | --- | --- | --- |
-| R-A A1 / A2 / A3 | 跑通（留一法 + 全集、六题回放、引擎头名、校准曲线、稳健性四项、LR 表 + 案例级 bootstrap 区间） | 头名 ±10 0.70 / 0.75 / 0.80（基线 0.80）；±30 0.55 / 0.60 / 0.65（0.55）；±60 0.45 / 0.55 / 0.50（0.40）。真值在区间除 ±60 A3 0.95 外全 1.00。`percent` 口径 softmax 后验过自信，挤出 3–5 例 | `pending_v5` |
-| R-B 缺席 @0.5 / 1.0 / 2.0 分 | 跑通（含漏说 1–2 件 × 5 次） | 2.0 分（=卡片）把覆盖压到 0.85 / 0.70 / 0.40；0.5 / 1.0 分覆盖 1.00 但头名一律降 | `pending_v5`（v4 信号为负） |
-| R-C C1 / C2 × prior / probe / both | 跑通 | 可追问件数每例均值 0.20 / 0.45 / 0.70；±30 上 C1_prior 7 例头名 0.71 | `pending_v5`（样本太少） |
+| R-A A1 | 76 / 0.571、77 / 0.455、72 / 0.247 | no_benefit ×3 | **不过**（头名三档降；±60 丢 3 例） |
+| R-A A2 | 75 / 0.545、76 / 0.416、72 / 0.247 | no_benefit ×3 | **不过** |
+| R-A A3 | 77 / 0.597、77 / 0.468、75 / 0.221 | no_benefit ×3 | **不过**（覆盖不降但头名三档降） |
+| R-B @0.5 分 | 77 / 0.662、75 / 0.442、75 / 0.221 | no_benefit ×3 | **不过**（头名降、宽度不降） |
+| R-B @1.0 分 | 73 / 0.649、71 / 0.429、70 / 0.195 | no_benefit ×3 | **不过** |
+| R-B @2.0 分（卡片强度） | 55 / 0.481、33 / 0.169、19 / 0.091 | no_benefit ×3 | **不过**（真值大量挤出） |
+| R-C（与同子集 C0 比） | C1_prior：20/20 头名 0.70 vs 0.75、34/34 0.47 vs 0.47（宽度 36 vs 37）、44/44 0.36 vs 0.42；C1_probe：0.60 vs 0.75、0.41 vs 0.47、0.38 vs 0.42；C2_prior：0.70 vs 0.80、0.42 vs 0.50、0.48 vs 0.52 | 只有 ±30 C1_prior 一格 benefit，其余 no_benefit | **不过**（无一致收益，不立实现单） |
 
-各特征在 v4 上的 LR 与「LR≈1」名单在报告 R-A 第 3 条与 JSON `radii.*.lr_table` / `noise_features`：**42 个特征里 31 / 36 / 38 个（±10 / ±30 / ±60）的 5–95% 区间跨 0**。
+`percent` 口径六格同样全部不过（R-A A1 76 / 77 / 77、A2 71 / 76 / 77、A3 66 / 75 / 76，头名全低于基线 0.79 / 0.81 / 0.77；R-B @2.0 分 54 / 33 / 20）。
 
-## 复跑与确定性（任务书硬红线 5）
+- 稳健性（R-A 留一法 `raw`，真值在区间）：±7 天 0.94–1.00、±1–3 月 0.94–1.00、答错 1 题 0.90–1.00、答错 2 题 0.82–0.98；没有一项补回头名损失。
+- 缺口 3：按方案矩阵重出六题的回放与共用题目**逐格相同**（出题器按代表候选与静态 context 判定分边，不读矩阵分数；每例题数 4.9 / 7.1 / 7.3）。对判定方向无影响，关闭。
+- 后验标定（缺口 2）：主判 `raw`（scale 只从训练折估，进 `unionStillValidRange`，与三轮既有研究同口径）；`percent` 口径的 Platt 温度在 v5 上拟合到 4–32，校准曲线在 ±30 / ±60 几乎全落在 [0,0.1) 桶且预测≈实际——过自信消失的代价是后验≈均匀，即先验不携带信息。两口径判定方向一致。
+- 特征：42 个里 36 / 38 / 39 个 bootstrap 5–95% 区间跨 0；剩余 |log LR| ≤ 0.15（D60 命中 −0.15、Shadbala 支持 −0.06 / 压力 +0.05、KP 星主 +0.06–0.09、KP 上升 AD 子主 −0.11）；**v4 与 v5 都不跨 0 且同号的特征：0 个**。
 
-- `PYTHONHASHSEED=0 python3 scripts/research/scoring_research.py --cache-dir <scratch>` 连跑两次（758 s / 748 s，静态 context 走磁盘缓存），三份 JSON `cmp` **逐字节一致**；`--out-suffix rerun` 的副本比对后删除，不入库。
-- JSON 不写耗时字段，`sort_keys=True`；随机（漏说抽样、月份平移、答错抽样、bootstrap）全部按 `f"{SEED}:{case_id}:{radius}:…"` 种子。
+### 文档
+
+- 研究报告改写为 v5 判定版（§1 结论表、§2.1 标定口径、§2.2 / §3.1 六格表、§2.4 特征表、§4 R-C、§6 v4 数字保留）。
+- `docs/BUG_HISTORY.md` BUG-1091 → `closed_by_design`（附数字）；定论页 §9 追加 v5 结论；`docs/research/ACTIVE_FRONTS.md` 索引已指向报告；`docs/tasks/README.md` 状态行更新。
 
 ## 快速门
 
-- 开工基线（`origin/staging` @ `6e393780`，未改任何文件）与改后各跑一次 `python3 scripts/run_quality_gate.py --profile quick`（PATH 带 Node 22 shim）：
-  - Python：**1001 passed, 1 skipped** 两次相同（改后含新增的 10 条 `test_scoring_research.py`——门禁的 pytest 集合按清单收集，新文件不在清单里，计数没变；定向 `python3 -m pytest tests/test_scoring_research.py -q` 10 passed）。
-  - 前端 `npm test`：基线 4357 / pass 4301 / fail 25；改后 4357 / pass **4302** / fail **24**、cancelled 0。失败清单逐条比对：改后 24 条是基线 25 条的子集（少的一条 `D3 · the first stage line shows on send…` 是基线那次偶发红、改后绿，前端零改动）；24 条全是需要 Docker / PostgreSQL / 部署环境的套件（Better Auth / billing / migration / staging sync 等），与 09-29 上一单验收记录的 24 条环境失败一致。
-  - 门禁整体 exit 1 = 该 24 条环境失败所致，基线同样 exit 1；**无新增失败**。
-- 隐私：`tests/test_repo_privacy_markers.py` 在快速门 Python 步内通过（1001 passed 含之）。
+- 阶段一：基线（`6e393780`）与改后各一次：Python 1001 passed / 1 skipped 两次相同；前端 4357 / pass 4302 / fail 24 / cancelled 0（基线 4301 / 25，改后失败清单是基线的子集，24 条全是 Docker / PostgreSQL / 部署环境套件）。
+- 阶段二（`0120765f` 之上，改动只在 `scripts/research/scoring_research.py` 与文档）：`python3 scripts/run_quality_gate.py --profile quick`（PATH 带 Node 22 shim）——Python **1001 passed / 1 skipped**（与阶段一两次相同；定向 `tests/test_scoring_research.py` 10 passed）；前端 **4391 / pass 4336 / fail 24 / cancelled 0**（staging 在 `6e393780`→`0120765f` 间新增 34 条测试，全部通过），24 条失败清单与阶段一逐条相同（Docker / PostgreSQL / 部署环境套件），门禁整体 exit 1 由此导致；**无新增失败**。
 
 ## 缺口与说明
 
-- **判定未做**：按任务书，R-A / R-B / R-C 的有无收益只能在 v5 上判；本轮 JSON 的 `debug_verdict_v4` 只是 `gate_verdict` 的机械输出，不作结论。
-- R-A 的 `percent` 口径需要先解决后验标定（单温度 Platt 不够，校准曲线高桶过自信）；v5 上若仍过自信，考虑按训练折做等分位标定或直接用 `raw` 口径（缩放到线上极差）。
-- 六道题由线上矩阵出一次、各方案共用（隔离先验的影响）；若某方案在 v5 上过门，实现前还应按方案矩阵重新出题再测一次。
-- 大运边界邻近项（`merge_transition_proximity`）按线上原样附加、未重加权。
-- R-B 的缺席年在 v4 上不可靠（传记摘录不是完整时间线）；这条限制在真人口述上同样存在，报告已写明。
-- 未做：R-C 的产品层实现、任何实现单；`CHANGELOG.md` 无用户可见变化，未改。
-
-## Claude 验收（2026-09-29，脚手架阶段）
-
-- 范围：`git diff --name-only` 在 `scripts/rectification/`、`active_rectification_event_engine.py`、`frontend/` 上为 0 个文件；三份结果 JSON 的 `verdict` 全部 `pending_v5`、`dataset: v4`。
-- 测试：`test_scoring_research.py` + `test_rectification_validation_integrity_gate.py` + `test_repo_privacy_markers.py` + `test_minute_resolution_research.py` + `test_cluster_width_research.py` = 117 passed（`PYTHONHASHSEED=0`）。
-- 已 rebase 到 `origin/staging`（BUG_HISTORY 与报告两单、上游同步 3 同时追加，冲突按编号顺序保留两侧）。
-- 结论：脚手架通过，合入 staging。判定阶段等 `TASK-rectification-holdout-expansion-20260929` 交付 v5 后再跑；届时先解决 `percent` 口径后验过自信（执行方缺口 2），并按方案矩阵重出六题（缺口 3）。
+- R-A / R-B 判定为负，**不立实现单**；BUG-560 维持 blocked。
+- R-C 与同子集基线比无一致收益，不立实现单；产品若仍想做"只追问一件落在候选分歧点的经历"，是交互层面的决定（额度按 09-26「定向追问 2 条」），本研究给不出精度理由。
+- 六道题由线上矩阵出一次、各方案共用；已验证按方案矩阵重出题结果相同。
+- 大运边界邻近项按线上原样附加、未重加权（R-A 判负后无需再测）。
+- 本机无 `.venv`，用系统 python3；`frontend/node_modules` 为软链，未入库。
diff --git a/docs/tasks/README.md b/docs/tasks/README.md
index 07c00b74..d21f0b67 100644
--- a/docs/tasks/README.md
+++ b/docs/tasks/README.md
@@ -256,7 +256,7 @@
 | `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` | `PROGRESS-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 | 已验收（Claude 2026-09-29 合并验收：全量前端 4302/24 与基线同 24 条环境失败、tsc 0、lint 0 error、`/` Static、gzip +0.16%、快速门 pytest 1001 passed、两份回放复跑一致），待部署核对 | BUG-1084～1087 |
 | `TASK-rectification-holdout-expansion-20260929.md` | `PROGRESS-rectification-holdout-expansion-20260929.md` | **开放评价集扩到 ≥60 例（v5）**：所有打分判定都在 20 例上做，1 例 = 5pp，KP / 精度闸 / V1n 的 no_benefit 都只差 1–3 例。只用 Rodden AA 公开名人，事件人工核对出处，分层（年代 / 纬度 / 南半球 / 跨午夜 / UTC+8），v4 不动；基线成绩单 v4 子集须与已发布数字逐格一致。研究单的判定以 v5 为准 | 已验收（Claude 09-29：77 例、v4 子集数字逐格一致、抽样 6 例来源一致、冻结文件零改动；UTC+8 5/6 为数据可得性缺口，接受；已合入 staging）→ 研究单判定可开工 | 分支 `codex/rectification-holdout-expansion-20260929`（BUG-1090） |
-| `TASK-rectification-scoring-research-20260929.md` | `PROGRESS-rectification-scoring-research-20260929.md` | **打分方法研究（离线）**：R-A 似然比校准权重（leave-one-case-out，KP / Pranapada / D60 作为特征由数据定权重、允许负权重）、R-B 缺席证据（口述时间线覆盖时段内的空白年扣分，必测漏说稳健性）、R-C 精度追问可行性（对已说事件追问月份，算在定向追问 2 条额度内）。判定必须在 v5 上做；有收益才立实现单并按 ERR-110 重冻结 | 脚手架已验收（Claude 09-29：对账 60 case-radius 0 差、JSON 复跑逐字节一致、冻结文件零改动、117 测试全绿；已合入 staging）；R-A / R-B / R-C 判定待 v5 | 分支 `codex/rectification-scoring-research-20260929`（BUG-1091 investigating） |
+| `TASK-rectification-scoring-research-20260929.md` | `PROGRESS-rectification-scoring-research-20260929.md` | **打分方法研究（离线）**：R-A 似然比校准权重（leave-one-case-out，KP / Pranapada / D60 作为特征由数据定权重、允许负权重）、R-B 缺席证据（口述时间线覆盖时段内的空白年扣分，必测漏说稳健性）、R-C 精度追问可行性（对已说事件追问月份，算在定向追问 2 条额度内）。判定必须在 v5 上做；有收益才立实现单并按 ERR-110 重冻结 | 脚手架已验收（Claude 09-29：对账 60 case-radius 0 差、JSON 复跑逐字节一致、冻结文件零改动、117 测试全绿；已合入 staging）；**v5 判定已做待验收**（Claude fork 09-30，77 例留一法）：R-A / R-B 六格全部不过硬红线 1、无稳定特征，打分层不立实现单（BUG-1091 closed_by_design）；R-C 同子集比无一致收益；对账 231/231 差 0，两次复跑逐字节一致 | 分支 `codex/rectification-scoring-research-20260929`（BUG-1091 closed_by_design） |
 | `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） | 已验收（Claude 2026-09-29 合并验收：全量前端 4302/24 与基线同 24 条环境失败、tsc 0、lint 0 error、`/` Static、gzip +0.16%、快速门 pytest 1001 passed、两份回放复跑一致），待部署核对 | BUG-1088、1089 |
 | `TASK-report-reader-polish-20260929.md` | `PROGRESS-report-reader-polish-20260929.md` | **报告页打磨**：生成入口挪进页面主体（删标题栏按钮）、详情页到底部按钮（懒渲染一次到底）、导出按钮带文字、目录一级/二级分层、去掉「字段」与 RL/NL 缩写表头、状态列同义重复去重、报告表格淡底色、分块导出显示真实文件大小 | Claude 直接执行并自验（tsc 0、lint 0 error、全量 fail 与基线同 24 条、`/` Static、gzip +7 B、快速门 Python 1000 passed），已部署 staging `d7772011`，真机欠 | BUG-1092～1094 |
 | `TASK-report-english-edition-20260929.md` | `PROGRESS-report-english-edition-20260929.md` | **报告中英两版**：同一次引擎计算渲染 zh/en 两遍（不用模型翻译），瑜伽库 477 条补英文、模板与前端表头英文化；中文逐字节不变、英文零汉字、两版数字序列一致；阅读页 `?lang=en` 切换，导出当前语言 + 「问 AI 建议导出英文版」提示；旧报告不补英文 | Claude 直接执行（含两个 fork 子代理）并自验（tsc 0、lint 0 error、全量 fail 与基线同 24 条、`/` Static、gzip +0.06%、Python 定向全绿），已部署 staging `d7772011`，真机欠 | — |
diff --git a/scripts/research/scoring_research.py b/scripts/research/scoring_research.py
index bf2487c0..9e51e4b2 100644
--- a/scripts/research/scoring_research.py
+++ b/scripts/research/scoring_research.py
@@ -29,6 +29,7 @@ Run:
 from __future__ import annotations
 
 import argparse
+import gc
 import json
 import pickle
 import sys
@@ -49,6 +50,7 @@ from scripts.research.minute_resolution_sweep import MINUTE_STEP, scoring_reques
 from scripts.research.offline_research_20260926_lib import exhaustive_flip_sets, flipped_answers, sample_flip_sets  # noqa: E402
 from scripts.research.precision_gate_lib import gate_verdict, jitter_day_events  # noqa: E402
 from scripts.research.probe_supply_after_six import ASK_COUNT, optimal_answer  # noqa: E402
+from scripts.active_rectification_events import precision_weight  # noqa: E402
 import scripts.research.scoring_research_lib as lib  # noqa: E402
 
 HOLDOUT_V4 = ROOT / "references" / "real_case_calibration" / "minute_rectification_holdout_v4.json"
@@ -115,12 +117,34 @@ class CaseData:
     def examples(self, *, include_extras: bool) -> list[tuple[dict[str, float], bool]]:
         out: list[tuple[dict[str, float], bool]] = []
         truth = set(self.truth_times)
-        for event_id in self.training_ids:
-            matrix = lib.event_feature_matrix(self.store, event_id, self.times, include_extras=include_extras)
+        for matrix in self.feature_matrices(include_extras=include_extras).values():
             for time in self.times:
                 out.append((matrix.get(time, {}), time in truth))
         return out
 
+    def feature_matrices(self, *, include_extras: bool) -> dict[str, dict[str, dict[str, float]]]:
+        cache = self.__dict__.setdefault("_fm_cache", {})
+        if include_extras not in cache:
+            cache[include_extras] = {
+                event_id: lib.event_feature_matrix(self.store, event_id, self.times, include_extras=include_extras)
+                for event_id in self.training_ids
+            }
+        return cache[include_extras]
+
+    def proxy_rows(self, table: lib.LRTable, variant: str) -> list[dict[str, Any]]:
+        """Linear proxy of `weighted_rows` for fitting scale / temperature (no matrix build)."""
+        include_extras, allow_negative = lib.VARIANT_SPEC[variant]
+        universe = [n for n in sorted(table.present) if (include_extras or not lib.is_extra_feature(n))]
+        matrices = self.feature_matrices(include_extras=include_extras)
+        rows = []
+        for time in self.times:
+            total = 0.0
+            for event_id, matrix in matrices.items():
+                weight = precision_weight(str(self.events_by_id[event_id]["precision"]))
+                total += weight * lib.weighted_points(matrix.get(time, {}), table, allow_negative=allow_negative, universe=universe)
+            rows.append({"time": time, "score": round(total, 6)})
+        return rows
+
     # scoring with a weight table ------------------------------------------
     def weighted_rows(
         self, table: lib.LRTable, variant: str, scale: float,
@@ -149,13 +173,13 @@ class CaseData:
         return score_from_matrix(request, built)
 
 
-def evaluate(data: CaseData, rows: Sequence[dict[str, Any]], *, softmax_prior: bool, pre_answers=(), answers=None, temperature: float = 1.0) -> dict[str, dict[str, Any]]:
+def evaluate(data: CaseData, rows: Sequence[dict[str, Any]], *, softmax_prior: bool, pre_answers=(), answers=None, temperature: float = 1.0, probes=None) -> dict[str, dict[str, Any]]:
     public = lib.public_for(rows, data.contexts)
     out: dict[str, dict[str, Any]] = {}
     for mode in PRIORS:
         prior = lib.prior_for(public, mode=mode, softmax=softmax_prior, temperature=temperature)
         result = lib.replay(
-            public=public, prior=prior, probes=data.probes, true_time=data.true_time,
+            public=public, prior=prior, probes=data.probes if probes is None else probes, true_time=data.true_time,
             window_times=data.times, pre_answers=pre_answers, answers=answers,
         )
         result["engine_top1"] = lib.engine_top1(public, data.true_time)
@@ -173,9 +197,12 @@ def fit_prior(training: Sequence[CaseData], table: lib.LRTable, variant: str) ->
     scale: research within-case score range matched to the production median
     range (so the raw-prior replay's ±2 / 8-lead constants mean the same);
     temperature: single Platt temperature for the percent (softmax) prior.
+    Training-case scores use the linear proxy (`CaseData.proxy_rows`: feature
+    weights × precision weight, no kind factor / transition proximity) so a
+    fold costs O(n) dictionary sums instead of n contribution-matrix builds.
     """
     prod = [lib.within_case_range(d.prod_rows) for d in training]
-    rows_by_case = {d.case_id: d.weighted_rows(table, variant, 1.0)[0] for d in training}
+    rows_by_case = {d.case_id: d.proxy_rows(table, variant) for d in training}
     research = [lib.within_case_range(rows) for rows in rows_by_case.values()]
     p_med, r_med = lib.median(prod), lib.median(research)
     scale = round(p_med / r_med, 6) if p_med and r_med else 1.0
@@ -186,8 +213,8 @@ def fit_prior(training: Sequence[CaseData], table: lib.LRTable, variant: str) ->
     return scale, lib.fit_temperature(publics)
 
 
-def run_ra(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any]:
-    out: dict[str, Any] = {"variants": list(lib.VARIANTS), "alpha": lib.LAPLACE_ALPHA, "radii": {}}
+def run_ra(by_radius: dict[int, list[CaseData]], *, fast: bool, probes_per_variant: bool = False) -> dict[str, Any]:
+    out: dict[str, Any] = {"variants": list(lib.VARIANTS), "alpha": lib.LAPLACE_ALPHA, "probes_per_variant": probes_per_variant, "radii": {}}
     for radius, cases in sorted(by_radius.items()):
         block: dict[str, Any] = {"n": len(cases), "baseline": {}, "full_fit": {}, "loo": {}, "robustness": {}, "calibration": {}, "lr_table": {}, "noise_features": {}}
         baseline_rows = []
@@ -195,6 +222,7 @@ def run_ra(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
             row = evaluate(data, data.prod_rows, softmax_prior=False)
             baseline_rows.append({"case_id": data.case_id, **{f"{m}": row[m] for m in PRIORS}})
         block["baseline"] = {m: lib.summarize([r[m] for r in baseline_rows]) for m in PRIORS}
+        block["per_case"] = {"baseline": [{"case_id": r["case_id"], **{m: {k: r[m][k] for k in ("top1", "coverage", "width", "engine_top1")} for m in PRIORS}} for r in baseline_rows]}
         per_case_examples = {
             include: {d.case_id: d.examples(include_extras=include) for d in cases} for include in (False, True)
         }
@@ -241,8 +269,13 @@ def run_ra(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
                 table = lib.estimate_lr([ex for d in training for ex in examples[d.case_id]])
                 scale, temperature = fit_prior(training, table, variant)
                 temperatures.append(temperature)
-                rows, _ = held.weighted_rows(table, variant, scale)
+                rows, built_v = held.weighted_rows(table, variant, scale)
                 result = evaluate(held, rows, softmax_prior=True, temperature=temperature)
+                if probes_per_variant:
+                    own = lib.production_probes(held.request, built_v, held.times, held.true_time)
+                    result["own_probes"] = evaluate(held, rows, softmax_prior=True, temperature=temperature, probes=own)
+                    result["own_probes_count"] = len(own)
+                result["case_id"] = held.case_id
                 loo_rows.append(result)
                 if variant == "A3":
                     public = lib.public_for(rows, held.contexts)
@@ -283,9 +316,25 @@ def run_ra(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
                             ))
             block["loo"][variant] = {m: lib.summarize([r[m] for r in loo_rows]) for m in PRIORS}
             block["loo"][variant]["temperatures"] = sorted(set(temperatures))
-            block["loo"][variant]["debug_verdict_v4"] = {
+            block["loo"][variant]["gate_verdict"] = {
                 m: gate_verdict(block["baseline"][m], block["loo"][variant][m]) for m in PRIORS
             }
+            block["loo"][variant]["hard_line_1"] = {
+                m: {
+                    "coverage_count": sum(1 for r in loo_rows if r[m]["coverage"]),
+                    "baseline_coverage_count": sum(1 for r in baseline_rows if r[m]["coverage"]),
+                    "top1": block["loo"][variant][m]["top1"],
+                    "baseline_top1": block["baseline"][m]["top1"],
+                    "pass": (sum(1 for r in loo_rows if r[m]["coverage"]) >= sum(1 for r in baseline_rows if r[m]["coverage"])
+                             and float(block["loo"][variant][m]["top1"]) + 1e-9 >= float(block["baseline"][m]["top1"])),
+                    "note": "truth and opposite cells coincide under D1 (nothing is injected after the six probes)",
+                }
+                for m in PRIORS
+            }
+            if probes_per_variant:
+                block["loo"][variant]["own_probes"] = {m: lib.summarize([r["own_probes"][m] for r in loo_rows]) for m in PRIORS}
+                block["loo"][variant]["own_probes"]["mean_count"] = round(sum(r["own_probes_count"] for r in loo_rows) / max(len(loo_rows), 1), 2)
+            block["per_case"][variant] = [{"case_id": r["case_id"], **{m: {k: r[m][k] for k in ("top1", "coverage", "width", "engine_top1")} for m in PRIORS}} for r in loo_rows]
             block["loo"][variant]["verdict"] = "pending_v5"
             if robustness:
                 block["robustness"][variant] = {
@@ -356,7 +405,13 @@ def run_rb(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
         for arm in block["arms"]:
             if arm == "B0":
                 continue
-            block["arms"][arm]["debug_verdict_v4"] = {m: gate_verdict(block["arms"]["B0"][m], block["arms"][arm][m]) for m in PRIORS}
+            block["arms"][arm]["gate_verdict"] = {m: gate_verdict(block["arms"]["B0"][m], block["arms"][arm][m]) for m in PRIORS}
+            block["arms"][arm]["hard_line_1"] = {m: {
+                "coverage_count": sum(1 for r in rows_by_arm[arm] if r[m]["coverage"]),
+                "baseline_coverage_count": sum(1 for r in rows_by_arm["B0"] if r[m]["coverage"]),
+                "pass": sum(1 for r in rows_by_arm[arm] if r[m]["coverage"]) >= sum(1 for r in rows_by_arm["B0"] if r[m]["coverage"])
+                        and float(block["arms"][arm][m]["top1"]) + 1e-9 >= float(block["arms"]["B0"][m]["top1"]),
+            } for m in PRIORS}
             block["arms"][arm]["verdict"] = "pending_v5"
         out["radii"][str(radius)] = block
     return out
@@ -376,8 +431,12 @@ def run_rc(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
             raw_events = list(data.case.get("events") or [])
             spoken = lib.degrade_to_year(raw_events)
             spoken_rows = data.production_rows_for(spoken)
-            rows_by_arm["B0_true_precision"].append(evaluate(data, data.prod_rows, softmax_prior=False))
-            rows_by_arm["C0_spoken"].append(evaluate(data, spoken_rows, softmax_prior=False))
+
+            def keep(arm: str, result: dict[str, dict[str, Any]]) -> None:
+                rows_by_arm[arm].append({"case_id": data.case_id, **result})
+
+            keep("B0_true_precision", evaluate(data, data.prod_rows, softmax_prior=False))
+            keep("C0_spoken", evaluate(data, spoken_rows, softmax_prior=False))
             # which spoken (year) events would split candidates if the month were known?
             training = set(data.training_ids)
             id_map = {str(e["id"]): str(rid) for rid, e in zip(
@@ -385,7 +444,7 @@ def run_rc(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
             )}
             candidates = []
             for event in raw_events:
-                if str(event.get("precision")) != "day":
+                if str(event.get("precision")) not in {"day", "month"}:
                     continue
                 request_id = id_map.get(str(event["id"]))
                 if request_id not in training:
@@ -406,10 +465,12 @@ def run_rc(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
                     continue
                 ids = {item[1] for item in picked}
                 upgraded = lib.upgrade_to_month(spoken, ids, originals)
-                rows_by_arm[f"C{k}_prior"].append(evaluate(data, data.production_rows_for(upgraded), softmax_prior=False))
+                upgraded_rows = data.production_rows_for(upgraded)
                 pre = [(item[2], "yes", 1.0) for item in picked]
-                rows_by_arm[f"C{k}_probe"].append(evaluate(data, spoken_rows, softmax_prior=False, pre_answers=pre))
-                rows_by_arm[f"C{k}_both"].append(evaluate(data, data.production_rows_for(upgraded), softmax_prior=False, pre_answers=pre))
+                keep(f"C{k}_prior", evaluate(data, upgraded_rows, softmax_prior=False))
+                keep(f"C{k}_probe", evaluate(data, spoken_rows, softmax_prior=False, pre_answers=pre))
+                keep(f"C{k}_both", evaluate(data, upgraded_rows, softmax_prior=False, pre_answers=pre))
+        by_case = {arm: {r["case_id"]: r for r in rows} for arm, rows in rows_by_arm.items()}
         block: dict[str, Any] = {
             "n": len(cases),
             "askable_per_case": {
@@ -419,11 +480,24 @@ def run_rc(by_radius: dict[int, list[CaseData]], *, fast: bool) -> dict[str, Any
                 "cases_with_at_least_two": sum(1 for c in askable_counts if c >= 2),
             },
             "arms": {arm: {m: lib.summarize([r[m] for r in rows]) for m in PRIORS} for arm, rows in rows_by_arm.items()},
+            "per_case": {arm: [{"case_id": r["case_id"], **{m: {k: r[m][k] for k in ("top1", "coverage", "width", "engine_top1")} for m in PRIORS}} for r in rows] for arm, rows in rows_by_arm.items()},
         }
-        for arm in block["arms"]:
+        for arm, rows in rows_by_arm.items():
             if arm.startswith("C0") or arm.startswith("B0"):
                 continue
-            block["arms"][arm]["debug_verdict_v4_vs_C0"] = {m: gate_verdict(block["arms"]["C0_spoken"][m], block["arms"][arm][m]) for m in PRIORS}
+            ids = [r["case_id"] for r in rows]
+            subset_c0 = [by_case["C0_spoken"][cid] for cid in ids]
+            subset_b0 = [by_case["B0_true_precision"][cid] for cid in ids]
+            block["arms"][arm]["subset_C0"] = {m: lib.summarize([r[m] for r in subset_c0]) for m in PRIORS}
+            block["arms"][arm]["subset_B0"] = {m: lib.summarize([r[m] for r in subset_b0]) for m in PRIORS}
+            block["arms"][arm]["gate_verdict_vs_subset_C0"] = {m: gate_verdict(block["arms"][arm]["subset_C0"][m], block["arms"][arm][m]) for m in PRIORS}
+            block["arms"][arm]["hard_line_1_vs_subset_C0"] = {m: {
+                "coverage_count": sum(1 for r in rows if r[m]["coverage"]),
+                "subset_C0_coverage_count": sum(1 for r in subset_c0 if r[m]["coverage"]),
+                "pass": sum(1 for r in rows if r[m]["coverage"]) >= sum(1 for r in subset_c0 if r[m]["coverage"])
+                        and float(block["arms"][arm][m]["top1"]) + 1e-9 >= float(block["arms"][arm]["subset_C0"][m]["top1"]),
+            } for m in PRIORS}
+            block["arms"][arm]["gate_verdict_vs_C0"] = {m: gate_verdict(block["arms"]["C0_spoken"][m], block["arms"][arm][m]) for m in PRIORS}
             block["arms"][arm]["verdict"] = "pending_v5"
         out["radii"][str(radius)] = block
     return out
@@ -453,6 +527,7 @@ def main() -> int:
     parser.add_argument("--fast", action="store_true", help="fewer repeats (development)")
     parser.add_argument("--no-write", action="store_true")
     parser.add_argument("--out-suffix", default="2026_09_29")
+    parser.add_argument("--probes-per-variant", action="store_true", help="also replay LOO variants with probes generated from their own matrix")
     args = parser.parse_args()
     started = clock_module.time()
     holdout_path = Path(args.holdout)
@@ -460,20 +535,41 @@ def main() -> int:
     if args.limit:
         cases = cases[: args.limit]
     cache_dir = Path(args.cache_dir) if args.cache_dir else None
-    by_radius: dict[int, list[CaseData]] = defaultdict(list)
     errors: list[dict[str, Any]] = []
     reconciliation: list[dict[str, Any]] = []
-    for case in cases:
-        for radius in args.radii:
+    parts = {"ra": "lr", "rb": "absence", "rc": "precision_followup"}
+    radii_results: dict[str, dict[str, dict[str, Any]]] = {key: {} for key in parts.values()}
+    part_meta: dict[str, dict[str, Any]] = {}
+    # One radius at a time: 77 cases x 3 radii of static contexts do not fit in memory together.
+    for radius in args.radii:
+        loaded: list[CaseData] = []
+        for case in cases:
             try:
                 data = CaseData(case, radius, cache_dir)
             except Exception as exc:  # noqa: BLE001
                 errors.append({"case_id": case.get("case_id"), "radius": radius, "error": f"{type(exc).__name__}: {exc}", "trace": traceback.format_exc()})
                 continue
             reconciliation.append({"case_id": data.case_id, "radius": radius, **data.reconciliation})
-            by_radius[radius].append(data)
-        print(f"loaded {case.get('case_id')} ({clock_module.time() - started:.0f}s)", flush=True)
-    unreconciled = [row for row in reconciliation if row["changed"]]
+            loaded.append(data)
+        print(f"loaded radius {radius}: {len(loaded)} cases ({clock_module.time() - started:.0f}s)", flush=True)
+        if any(row["changed"] for row in reconciliation if row["radius"] == radius):
+            print(json.dumps({"UNRECONCILED": [row for row in reconciliation if row["changed"]]}, ensure_ascii=False, indent=1))
+            return 2
+        by_radius = {radius: loaded}
+        runners = {
+            "ra": lambda: run_ra(by_radius, fast=args.fast, probes_per_variant=args.probes_per_variant),
+            "rb": lambda: run_rb(by_radius, fast=args.fast),
+            "rc": lambda: run_rc(by_radius, fast=args.fast),
+        }
+        for part, key in parts.items():
+            if part not in args.parts:
+                continue
+            block = runners[part]()
+            radii_results[key].update(block.pop("radii"))
+            part_meta[key] = block
+            print(f"{part.upper()} radius {radius} done {clock_module.time() - started:.0f}s", flush=True)
+        del loaded, by_radius
+        gc.collect()
     header = {
         "generated_for": "TASK-rectification-scoring-research-20260929",
         "dataset": args.dataset_label,
@@ -485,32 +581,22 @@ def main() -> int:
         "node_mode": NODE_MODE,
         "ask_count": ASK_COUNT,
         "probe_today": lib.TODAY.isoformat(),
-        "probes": "production G0 probes generated once per case/radius from the production matrix; reused for every arm",
+        "probes": "production G0 probes generated once per case/radius from the production matrix; reused for every arm (own_probes = regenerated from the variant matrix)",
         "open_set_not_blind": True,
-        "verdict": "pending_v5",
-        "reconciliation": {"cases": len(reconciliation), "unreconciled": len(unreconciled), "rows": reconciliation},
+        "verdict": "pending_v5" if args.dataset_label != "v5" else "see_report",
+        "reconciliation": {"cases": len(reconciliation), "unreconciled": sum(1 for r in reconciliation if r["changed"]), "rows": reconciliation},
         "errors": errors,
         "seed": SEED,
         "fast": bool(args.fast),
     }
-    if unreconciled:
-        print(json.dumps({"UNRECONCILED": unreconciled}, ensure_ascii=False, indent=1))
-        return 2
     results: dict[str, dict[str, Any]] = {}
-    if "ra" in args.parts:
-        results["lr"] = {**header, "part": "R-A", **run_ra(by_radius, fast=args.fast)}
-        print("R-A done", f"{clock_module.time() - started:.0f}s", flush=True)
-    if "rb" in args.parts:
-        results["absence"] = {**header, "part": "R-B", **run_rb(by_radius, fast=args.fast)}
-        print("R-B done", f"{clock_module.time() - started:.0f}s", flush=True)
-    if "rc" in args.parts:
-        results["precision_followup"] = {**header, "part": "R-C", **run_rc(by_radius, fast=args.fast)}
-        print("R-C done", f"{clock_module.time() - started:.0f}s", flush=True)
+    for part, key in parts.items():
+        if part in args.parts:
+            results[key] = {**header, "part": {"ra": "R-A", "rb": "R-B", "rc": "R-C"}[part], **part_meta.get(key, {}), "radii": radii_results[key]}
     for key, payload in results.items():
-        payload["elapsed_seconds"] = round(clock_module.time() - started, 1)
         if not args.no_write:
-            write_json(OUT_DIR / f"scoring_research_{key}_{args.out_suffix}.json", {k: v for k, v in payload.items() if k != "elapsed_seconds"})
-    brief: dict[str, Any] = {"reconciliation_unreconciled": len(unreconciled), "errors": len(errors)}
+            write_json(OUT_DIR / f"scoring_research_{key}_{args.out_suffix}.json", payload)
+    brief: dict[str, Any] = {"reconciliation_unreconciled": header["reconciliation"]["unreconciled"], "errors": len(errors), "elapsed_seconds": round(clock_module.time() - started, 1)}
     for key, payload in results.items():
         brief[key] = {}
         for radius, block in payload["radii"].items():