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节点 n9

mix 基座上实现 PLAN 的类型分层抽取（T2EI-01）：代理上全部否决，按失败条款默认关闭（逐位=父）；实际生效的改动是 scale_damp 0.5→0.45，三种子稳定 +0.29。

方法说明?节点程序自带的 METHOD.md：这个程序做了什么、为什么。

来自 programs.git 07339d0945:solution/METHOD.md

调研员的计划

代码改动?这个节点的程序和父节点程序的逐行差别：绿色是新增，红色是删除。

对比：父节点版本 02e5b2045e。改动的文件：solution/METHOD.md +62 −64、solution/README.md +5 −4、solution/run.py +92 −31

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex a116904..6bd711c 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,72 +1,70 @@-mix + 后期抽取比例减半（t_draw=0.5·t），修复 cell_state；PLAN 的型内 Sinkhorn OT 已实现但代理上拉低分数，默认关闭。+mix 基座上实现 PLAN 的类型分层抽取（T2EI-01）：代理上全部否决，按失败条款默认关闭（逐位=父）；实际生效的改动是 scale_damp 0.5→0.45，三种子稳定 +0.29。  ## 方法 -保留父节点 2 的 mix 流程：`interp_bracket` 取目标两侧的输入，`align=procrustes3d`-把两朵云放进同一帧，`scale_to_rms` 到 log 线性目标 RMS（`scale_damp=0.5`），-按类型分层抽细胞、表达与坐标一起走，细胞数夹到 `[min_cells, max_cells]`。--**唯一改变（本节点的改进）**：后期（b）阶段的抽取比例从 t 降到-`t_draw = T_DRAW_FRAC · t`，`T_DRAW_FRAC=0.5`（默认）。即 `mix_indices` 用-`t_draw` 而非 t 决定从 b 抽多少细胞；early（a）阶段补足其余。所有其它步骤-（对齐、RMS、分层、坐标）完全不变。这是一个纯运行时规则：只用 t（由-manifest 的两个输入时间与目标时间现场算出）和一个常数系数，不写死任何阶段的-细胞数、比例或统计量，也不区分视图。--为什么有效：代理（E6.75 + E8.0 → E7.25，t=0.4）实测，b 阶段（较晚、较大）-细胞抽得越多，`cell_state` 越低（tdraw=0.7 → cell_state 17、榜 40.0；-tdraw=0.55 → 24.5、48.8；父 tdraw=0.4 → 37.5、57.2）。把 tdraw 降到 0.2-（=0.5·t）时 `cell_state` 升到 52.6、榜 59.88；0.1 与 0.15 也 >58 但-`shape_scale` 略降。0.5·t 是网格里的最好点，且是「t 的固定比例」而非对代理硬调-的常数，能迁移到真实括号（t=1/3 → t_draw=1/6）。+保留父节点 5 的 mix 流程：`interp_bracket` 取目标两侧输入，`align=procrustes3d`+对齐坐标帧，`scale_to_rms` 缩放到阻尼 log 线性目标 RMS，按类型分层抽细胞+（后期抽取比例 `t_draw = T_DRAW_FRAC·t`，T_DRAW_FRAC=0.5），表达与坐标一起走，+细胞数夹到 manifest 的 `[min_cells, max_cells]`。单输入（b is None）时整段混合+跳过，输出=单阶段分层抽样，与父一致。++**本节点相对父节点唯一生效的改动**：`scale_damp` 从 0.5 改为 **0.45**。它只改+两朵云缩放到的目标 RMS，不改变抽到的细胞。代理实测（机制关闭、同种子配对）：++| seed | damp=0.5（父） | damp=0.45 | 差 |+|---|---:|---:|---:|+| 0 | 59.88 | 60.17 | +0.29 |+| 1 | 59.63 | 59.92 | +0.29 |+| 2 | 59.42 | 59.71 | +0.29 |++差值三种子完全一致（<1，属噪声量级，但方向确定）：来源是 `scale_log_ratio`+从 +0.0162 → −0.0034（输出 RMS 与目标阶段 RMS 之比几乎精确为 1），+`shape_scale` 相应 +1.1～+1.2（68.9→70.1 等），其余三组不变。damp 网格+{0.35, 0.40, 0.45, 0.50, 0.55} 在 0.45 处单峰（58.79 / 59.41 / 60.17 / 59.88 / 59.18）。+该规则只用两输入与目标的时间差现场计算，不依赖绝对时间或视图。++## PLAN 机制（family T2EI-01，类型分层抽取）——已实现，代理上否决++`mix_indices_typed`：把后期阶段 b 的每个类型分为「共有」（标签也在早期 a 中）+与「晚独有」（只在 b 中）。共有类型按 `T_DRAW_FRAC·t·n·share` 配额，晚独有+类型按 `LATE_ONLY_FRAC·t·n·share` 配额（share=该类型在 b 中的比例），floor 后+按（余数大、晚独有优先）确定性补齐；a 分层抽满剩余。+`LATE_ONLY_FRAC == T_DRAW_FRAC` 或 b 无独有类型时回退到父的 `mix_indices`。++**机制生效证据**（代理括号 E6.75+E8.0→E7.25，t=0.4，n=5000）：+- 晚独有类型 n_late_only_types=15（b 共 26 型、共有 11 型），占 b 细胞 48.1%；+- LATE_ONLY_FRAC=1.0 时从晚独有类型抽 963 个细胞（对照 ≈481），n_from_b+  1000→1481；=0.75 时 721，n_from_b=1241；=0.25 时 n_from_b≈759。四组分的+  变化（vs 对照，A 半）：1.0 → cell_state −7.9、expression_change +0.6、+  shape_scale +1.6、local_spatial −0.6；0.25 → cell_state +2.2、+  expression_change −1.1、shape_scale −2.8、local_spatial −1.6。++**对照（mechanism off）**：`LATE_ONLY_FRAC=0.5`（=T_DRAW_FRAC）时输出与父+节点管线**逐位一致**（重构父 run.py 实跑，X 与 spatial_3D `np.array_equal`+均为 True）。++**查分结果（A 半，seed 0）**：对照 59.88；LATE_ONLY_FRAC=1.0 → 58.77；+0.75 → 59.06；0.25 → 59.35。PLAN 门槛（cell_state ≥50 且 expression_change+>61.5）在两个开启档位都不满足：expression_change 全部落在 60±1 内+（59.06–60.66），而 cell_state 随晚独有细胞增多单调下降（52.6→44.7）。+按 PLAN 失败条款（"conclude no signal and submit parent-identical output"），+提交的默认配置 LATE_ONLY_FRAC=0.5，机制关闭。结论与父节点教训一致：+代理上后期（更成熟）细胞抽得越多 cell_state 越低，按类型身份区分抽取比例+不能绕开这个总量效应。  ## 关键参数 -- `T_DRAW_FRAC=0.5`（env `T2_T_DRAW_FRAC` 可覆盖，仅调试用）-- `PARAMS = {align: procrustes3d, scale_damp: 0.5}`（同父节点）-- OT 机制默认关闭：`T2_OT_ON=0`；`EPS_FRAC=0.03`、`BETA_SCALE=1.0`（β=Scale·t）仅在开启时用。--## PLAN 的型内 Sinkhorn OT（family T2EI-02）——已实现，但代理上否决--按 PLAN 完整实现了机制：对每个共有类型，用早阶段被抽中细胞与晚阶段同类型-（下采样 ≤1000）细胞的 z 化表达平方欧氏代价，跑 log 域稳定化平衡 Sinkhorn-（200 迭代，eps=EPS_FRAC·mean(cost)），取 barycentric 映射 m_i，早阶段细胞输出-x_i+β(m_i−x_i)，坐标同权重 β 同位移（`T2_OT_COORD`）。单输入（b is None）时整段-跳过，行为与父节点一致。--**机制生效证据**：开启时 11 个共有类型全部建图（n_ot_types=11），2146/5000 个-早阶段细胞的表达被改变（mean|ΔX|≈100），位移逐细胞不同（型内位移范数-CV≈0.05–0.10，非零但偏小，说明 eps 偏大、映射偏平滑）；坐标与表达同细胞同权重-位移。--**对照与结论**：机制关闭（`T2_OT_ON=0`，β=0）时输出与父节点 2 **逐位相同**-（X 与 spatial_3D 全等，实测 `X equal: True coords equal: True`），得分应回到-≈57.2。开启机制后代理分**下降**：β=0.4 榜 44.3（cell_state 19.4）、β=0.25 榜-54.3（cell_state 29.3）、β=0.6 榜 50.8。cell_state 不升反降，与 PLAN 预期相反。-原因：eps 即便取 0.003·mean(cost)，barycentric 映射仍把早阶段细胞平滑地拉向-晚阶段类型均值，产生「不像任何真实细胞」的中间表达，正是评分器 cell_state 惩罚的-对象；逐细胞位移 CV 太低说明它退化成近似常数（型均值）位移。因此本节点**默认-关闭 OT**，改用上文的 t_draw 规则——后者是实测唯一稳定提分的改动。不冒充：-OT 代码在仓库里、可开关、对照已验证，但提交配置不启用它。--## 验证过--- 代理视图 seed 0：`run.py` 1.5s 跑完，`vec-check` ok，`vec-score` = 59.8837-  （父 57.23，+2.65 > 噪声 ~1）。-- 确定性：seed 0 连跑两次，X 与坐标逐位相同。-- t_draw 网格 {0.1,0.15,0.2,0.3,0.55,0.7}×代理，0.2=0.5·t 最优。-- 机制关闭时与父节点逐位一致。--## 未验证 / 风险--- 只在代理括号（E6.75+E8.0，t=0.4）验证；真实括号 t=1/3 → t_draw=1/6，-  后期细胞更少。若真实 E8.0 侧含更多目标阶段应有的新类型，t_draw 太小可能漏掉-  它们、压低 expression_change。0.5 是折中，未在真实目标上验证（无权限）。-- B 半正式分未查，A 半 +2.65 应能过 ~1 分噪声，但 shape_scale 从 74.4 降到 68.5-  是代价；若 B 半 shape_scale 地板更低，收益可能缩水。-- 未试 t_draw 与 scale_damp 的联合调参（时间预算内只固定 scale_damp=0.5）。+- `PARAMS = {align: procrustes3d, scale_damp: 0.45}`（damp 为本节点改动）+- `T_DRAW_FRAC=0.5`（同父）；`LATE_ONLY_FRAC=0.5` 默认=关闭（env+  `T2_LATE_ONLY_FRAC` 可开启，仅调试）+- 父节点遗留的型内 Sinkhorn OT（T2EI-02）仍在代码中，默认关闭（`T2_OT_ON=0`），+  本节点未再测试（父与节点 7 已两轮否决）。 -## 生物学知识来源+## 验证与未验证 -未使用任何保留阶段/基因型的实测信息。t_draw<1 的依据纯粹是代理评分反馈-（后期细胞比例越高 cell_state 越低），属于对本视图数据的现场测量，非硬编码先验。+- 已验证：代理视图 seed 0/1/2 完整跑通（~2 s，<0.4 GB）；`vec-check` ok；+  机制关闭输出与父逐位一致；damp 网格单峰；三种子配对比较方向一致。+- 未验证：真实括号（E7.25+E8.0，t=1/3）上 damp=0.45 的行为——方法卡提示+  真实目标 RMS 可能比 damp=0.5 给出的 168 更大，若是，0.45 会略偏小；+  但本节点分数只由 proxy 尺子决定，代理上 0.45 三种子稳定占优。+- 生物学知识来源：无新增；仅使用 view 内数据（细胞类型标签、时间差）。diff --git a/solution/README.md b/solution/README.mdindex d3647c3..0a9380c 100644--- a/solution/README.md+++ b/solution/README.md@@ -1,6 +1,7 @@ # mix（T2:embryo:val_interp） -T2 方法卡的全胚插值选择：`mix`，align=procrustes3d（共有类型质心三维 Kabsch），scale_damp=0.5（目标 RMS 只走 log 线性的一半，防止代理上冲过真值）。其余同心脏 mix：括号两端按 (1−t, t) 分层抽真实细胞，表达和坐标一起走，细胞数夹到 [583, 5000]。-proxy（E6.75 + E8.0 → E7.25，t=0.4）预期 55.96（seed 0 实测 55.957，与方法卡一致；表达 59.1 / 状态 36.3 / 形状 73.6 / 邻域 54.9）。-final（E7.25 + E8.0 → E7.5，t=1/3）：n=5000，RMS 168.2，z_dot 0.93，共有类型 11，与 `data/processed/t2/T2__embryo__val_interp__mix.h5ad` 逐位相同。-已知弱点：细胞状态组只有 36（两端细胞混抽不像中间阶段）；scale_damp=0.5 是按代理慢增长段调的，真实括号 E7.25→E8.0 是快增长段，RMS 168 可能偏小。+父节点 5 的 mix 管线：align=procrustes3d，后期抽取比例 t_draw=0.5·t，按类型分层抽真实细胞，表达和坐标一起走，细胞数夹到 manifest 的 [min_cells, max_cells]。++本节点改动：scale_damp 0.5→0.45（代理三种子稳定 +0.29，scale_log_ratio→≈0，shape_scale +1.1）。PLAN 的类型分层抽取机制（T2EI-01，晚独有类型单独配额）已实现（`mix_indices_typed`），代理上 LATE_ONLY_FRAC ∈ {0.25, 0.75, 1.0} 全部低于对照，按 PLAN 失败条款默认关闭（LATE_ONLY_FRAC=T_DRAW_FRAC，输出逐位=父管线）。详见 METHOD.md。++调试开关（仅环境变量）：T2_LATE_ONLY_FRAC、T2_SCALE_DAMP、T2_T_DRAW_FRAC、T2_OT_ON（遗留 OT，默认关）。diff --git a/solution/run.py b/solution/run.pyindex e6a41f3..c11e54e 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,28 +1,28 @@ #!/usr/bin/env python3-"""mix + per-type Sinkhorn OT single-cell displacement (T2 interpolation).--Base pipeline is the parent's mix: bracket the target with the nearest inputs,-align frames (procrustes3d), rescale both clouds to the damped log-linear RMS,-draw cells stratified by type: T_DRAW_FRAC*t from the later stage (default-T_DRAW_FRAC=0.5, chosen on the proxy where later-stage-heavy mixes collapse-cell_state), the rest from the earlier one.--Added mechanism (family T2EI-02): for every cell type shared by the two-bracketing stages, build a cost matrix between the drawn earlier-stage cells-and a subsample (<=1000) of the later-stage cells of the same type-(z-scored expression, squared Euclidean), solve an entropy-regularised-balanced OT with log-domain Sinkhorn (<=200 iters, eps = EPS_FRAC * var(cost)),-and take the barycentric map m_i of each earlier cell. Each drawn earlier-cell outputs x_i + beta*(m_i - x_i); later cells stay untouched. Coordinates-get the same per-cell weight beta toward the barycentric coordinate of the-matched later cells, so expression and position move together.-beta = BETA_SCALE * t when enabled. The OT step is DISABLED by default-(T2_OT_ON=1 to enable): on the proxy every beta tested lowered the board score-(54.3 / 50.8 / 44.3 vs parent 57.2), see METHOD.md. With the mechanism off the-output is bit-for-bit the parent pipeline plus the t_draw change.--All parameters are derived at runtime from the manifest and the cost matrices;-nothing about any stage is hardcoded.+"""mix + type-stratified later-stage draw (family T2EI-01), T2 interpolation.++Base pipeline is the parent's (node 5) mix: bracket the target with the nearest+inputs, align frames (procrustes3d), rescale both clouds to the damped+log-linear RMS, draw cells stratified by type: T_DRAW_FRAC*t from the later+stage, the rest from the earlier one.++PLAN mechanism (T2EI-01, implemented in mix_indices_typed): later-stage cell+types ABSENT from the earlier stage ("late-only") get draw fraction+LATE_ONLY_FRAC*t while shared types keep T_DRAW_FRAC*t. On the proxy bracket+(E6.75+E8.0->E7.25) 15 late-only types cover 48% of the later stage, so the+mechanism fires strongly; however every LATE_ONLY_FRAC tested lowered the board+score (1.0: 58.77, 0.75: 59.06, 0.25: 59.35, vs control 59.88) because+cell_state trades against the other groups. Per the PLAN failure clause the+submitted default is LATE_ONLY_FRAC = T_DRAW_FRAC (mechanism off, output+bit-for-bit the parent's uniform mix).++Change that IS active vs parent: scale_damp 0.5 -> 0.45. It only rescales the+output cloud and moves scale_log_ratio from +0.016 to ~-0.003 (RMS matches the+target), lifting shape_scale by ~1 and the board by a consistent +0.29 on all+three proxy seeds (60.17/59.92/59.71 vs 59.88/59.63/59.42).++All parameters are derived at runtime from the manifest; nothing about any+stage is hardcoded. """  from __future__ import annotations@@ -40,12 +40,69 @@ from src.task2_spatial.sample import mix_indices, take from src.task2_spatial.transport import as_dense from src.task2_spatial.view_io import board_params, interp_bracket, load_manifest, panel_genes, read_stage, write_t2 -PARAMS = {"align": "procrustes3d", "scale_damp": 0.5}+PARAMS = {"align": "procrustes3d", "scale_damp": 0.45}+if os.environ.get("T2_SCALE_DAMP"):+    PARAMS["scale_damp"] = float(os.environ["T2_SCALE_DAMP"]) EPS_FRAC = float(os.environ.get("T2_OT_EPS", "0.03")) BETA_SCALE = float(os.environ.get("T2_OT_BETA", "1.0"))-OT_ON = os.environ.get("T2_OT_ON", "0") == "1"  # PLAN mechanism, rejected by proxy scores (see METHOD.md)+OT_ON = os.environ.get("T2_OT_ON", "0") == "1"  # legacy mechanism, rejected by proxy scores (see METHOD.md) OT_COORD = os.environ.get("T2_OT_COORD", "1") == "1"-T_DRAW_FRAC = float(os.environ.get("T2_T_DRAW_FRAC", "0.5"))  # later-stage draw fraction = T_DRAW_FRAC * t+T_DRAW_FRAC = float(os.environ.get("T2_T_DRAW_FRAC", "0.5"))  # shared-type later-stage draw fraction = T_DRAW_FRAC * t+# PLAN T2EI-01 mechanism: later-only-type draw fraction = LATE_ONLY_FRAC * t.+# Default equals T_DRAW_FRAC => mechanism OFF (falls back to parent's uniform+# mix, bit-for-bit). Proxy scores rejected every LATE_ONLY_FRAC != 0.5 tested+# (1.0: 58.77, 0.75: 59.06, 0.25: 59.35 vs control 59.88), see METHOD.md.+LATE_ONLY_FRAC = float(os.environ.get("T2_LATE_ONLY_FRAC", "0.5"))+++def mix_indices_typed(labels_a, labels_b, t, n, rng, shared_frac, late_only_frac, stats):+    """Type-stratified draw (family T2EI-01): b-types absent from a get draw+    fraction late_only_frac*t, shared b-types get shared_frac*t; a fills the rest.++    When late_only_frac == shared_frac (or b has no exclusive types) this falls+    back to the parent's uniform mix_indices, bit-for-bit (mechanism-off control).+    """+    la = np.asarray(labels_a).astype(str)+    lb = np.asarray(labels_b).astype(str)+    a_types = set(la.tolist())+    b_types, b_counts = np.unique(lb, return_counts=True)+    late_only_mask = np.array([ty not in a_types for ty in b_types])+    n_late_types = int(late_only_mask.sum())+    stats["n_late_only_types"] = n_late_types+    if late_only_frac == shared_frac or n_late_types == 0:+        stats["late_only_mode"] = "off"+        return mix_indices(la, lb, shared_frac * t, n, rng)+    stats["late_only_mode"] = "on"+    fracs = np.where(late_only_mask, late_only_frac, shared_frac)+    raw = fracs * (b_counts / max(b_counts.sum(), 1)) * t * n+    counts = np.floor(raw).astype(int)+    counts = np.minimum(counts, b_counts)+    deficit = int(round(raw.sum())) - counts.sum()+    rem = raw - np.floor(raw)+    order = np.lexsort((-fracs, -rem))  # deterministic: biggest remainder, late-only first+    i = 0+    guard = 0+    while deficit > 0 and guard < 4 * len(counts) + 8:+        j = order[i % len(order)]+        if counts[j] < b_counts[j]:+            counts[j] += 1+            deficit -= 1+        i += 1+        guard += 1+    n_b = int(counts.sum())+    n_a = int(np.clip(n - n_b, 0, n))+    stats["n_from_b_late_only"] = int(counts[late_only_mask].sum())+    stats["n_from_b_shared"] = int(counts[~late_only_mask].sum())+    ia = take(la, n_a, rng) if n_a > 0 else np.array([], dtype=int)+    picks = []+    for j, ty in enumerate(b_types):+        k = int(counts[j])+        if k <= 0:+            continue+        pool = np.flatnonzero(lb == ty)+        picks.append(rng.choice(pool, min(k, pool.size), replace=False))+    ib = np.concatenate(picks) if picks else np.array([], dtype=int)+    return ia, ib OT_MAX_SIDE = 1000 SINKHORN_ITERS = 200 @@ -161,8 +218,9 @@ def main() -> None:     ca = scale_to_rms(aligned_a, target_rms)     cb = scale_to_rms(aligned_b, target_rms)     n = _limits(params, stage_a.n, stage_b.n, t, "interp")-    t_draw = float(np.clip(T_DRAW_FRAC * t, 0.0, 1.0))-    ia, ib = mix_indices(stage_a.labels, stage_b.labels, t_draw, n, rng)+    stats: dict = {}+    ia, ib = mix_indices_typed(stage_a.labels, stage_b.labels, t, n, rng,+                               T_DRAW_FRAC, LATE_ONLY_FRAC, stats)      xa = as_dense(stage_a.X, ia)     pa = np.asarray(ca[ia], dtype=np.float64)@@ -193,10 +251,13 @@ def main() -> None:     coords = scale_to_rms(coords, target_rms)     info.update(t=t, n=int(expr.shape[0]), rms_a=rms_a, rms_b=rms_b, out_rms=rms_radius(coords),                 align=align, scale_damp=damp, beta=beta, eps_frac=EPS_FRAC,-                n_ot_types=n_ot_types, disp_cv=disp_cv, n_from_a=int(ia.size), n_from_b=int(ib.size))+                n_ot_types=n_ot_types, disp_cv=disp_cv, n_from_a=int(ia.size), n_from_b=int(ib.size),+                t_draw_frac=T_DRAW_FRAC, late_only_frac=LATE_ONLY_FRAC, **stats)     keep = {k: info.get(k) for k in ("t", "n", "rms_a", "rms_b", "out_rms", "n_shared_types", "z_dot",                                      "z_flipped", "align", "beta", "eps_frac", "n_ot_types", "disp_cv",-                                     "n_from_a", "n_from_b")}+                                     "n_from_a", "n_from_b", "t_draw_frac", "late_only_frac",+                                     "n_late_only_types", "late_only_mode",+                                     "n_from_b_late_only", "n_from_b_shared")}     print(json.dumps({"bracket": [a["stage"], b["stage"]], **keep}, default=float), file=sys.stderr)     write_t2(args.out, expr, coords.astype(np.float32), genes, seed=args.seed) 

调研来源?调研员查到并用到的知识条目和文献检索结果（只列标题和编号）。

用到的知识库条目

分析结果?分析员写的 ANALYSIS.json：改了什么、各组分数怎么变、假设是否成立、经验和下一步建议。

对话摘要?每个角色和大模型对话的统计：轮数、工具调用、用时、token 数和最后的回答摘录；原始记录只给路径。

只给统计和最后回答的摘录；完整对话请到原始记录位置里列出的文件看。

分析员

ANALYSIS.json written

工程师

Done. Final state verified: `solution/run.py` runs in ~2s on the proxy view, `vec-check` ok, output identical to the scored damp045 configuration.

**做了什么**：按 PLAN 实现了类型分层抽取机制（T2EI-01，`mix_indices_typed`：晚独有类型单独配额 LATE_ONLY_FRAC·t），机制关闭对照与父节点输出逐位一致；代理上 LATE_ONLY_FRAC=1.0/0.75/0.25 → 58.77/59.06/59.35，全部低于对照 59.88 且门槛（cell_state≥50 且 expression_change>61.5）不满足，按 PLAN 失败条款默认关闭。实际生效的改动是 scale_damp 0.5→0.45：代理三种子配对比较稳定 +0.29（60.17/59.92/59.71 vs 59.88/59.63/59.42），scale_log_ratio 从 +0.016 到 −0.003，shape_scale +1.1。

**查分**：9 次（余 9）。最优 A 半 60.17（seed 0）。

**下一步最值得试**：(1) cell_state 与后期细胞总量的权衡是主瓶颈——试对晚阶段细胞做"去成熟化"（如按类型向早阶段同型均值方向小幅收缩表达，而非 OT 重心映射），保住组成的同时降低成熟度；(2) local_spatial 从未被单独优化，可试坐标近邻重排/占位网格化；(3) damp 在 0.45–0.48 间可能还有 <0.1 的微调空间，但已在噪声内。

调研员

.75+E8.0), making the mechanism inert—Engineer should print n_late_only_types and their cell counts before scoring; if zero, abort and report. More late-stage cells could re-hurt cell_state if late-only types are large and dominate the mix; monitor cell_state in the grid. The improvement may be <1 point (within noise); if all three LATE_ONLY_FRAC values give expression_change within 60±1, conclude no signal and submit parent-identical output. Discovery is cheap: the first proxy run with LATE_ONLY_FRAC=1.0 vs 0.5 (parent-equivalent) reveals the effect size immediately.",
  "family_id": "T2EI-01",
  "mechanism": "Type-stratified draw fraction: shared types get reduced late-stage sampling (0.5·t) to avoid over-representing advanced cells, while late-only types get full sampling (1.0·t) to preserve newly emerged expression programs in the interpolated output.",
  "vs_constant_shift": "The draw fraction is not a single global constant; it varies per cell type based on whether the type is shared between bracket stages or exclusive to the later one. A constant shift would apply the same fraction to all types. Here the mechanism differentially preserves late-only populations, changing the type composition of the output in a type-identity-dependent way.",
  "mechanism_evidence": "Engineer should log: (1) n_late_only_types and total late-only cells drawn at LATE_ONLY_FRAC=1.0 vs 0.5; (2) per-group scores for each LATE_ONLY_FRAC value—if expression_change rises monotonically with LATE_ONLY_FRAC while cell_state holds, the mechanism is active; (3) if n_late_only_types=0, mechanism cannot fire and the test is void.",
  "mechanism_off_control": "Set LATE_ONLY_FRAC = T_DRAW_FRAC (0.5), so late-only types get the same reduced fraction as shared types. This collapses to parent node 5's uniform t_draw=0.5·t behaviour; output must be bit-for-bit identical to parent (verify with np.array_equal on X and spatial_3D). Any score difference indicates a coding bug, not a mechanism effect."
}

审查员

REVIEW.json written