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节点 n10 在终选来历上

composition_trend + 型内表达重锚定，α 扫描延伸至拐点：α=3.0 出厂（α=2.5/3.0/3.5 → 节点分 55.78/55.87/55.72），新增按型收缩开关 k（出厂关）

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 da1e39c0bf: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 634b71af7c。改动的文件：solution/METHOD.md +70 −81、solution/run.py +23 −9

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex ce3b6bd..35aa650 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,94 +1,83 @@-# composition_trend + 型内表达重锚定：加权抽样后按型把表达均值锚回输入全细胞均值（α=2 出厂，α=0 为关闭对照）--Parent (node 4 = node 3 composition_trend with the λ-shift disabled): type-level composition-reweighting + within-type maturity selection (A_CM=1.2, A_FATE=0.6) of real input cells;-expression never modified. This node adds, per PLAN (family `composition_program`), **per-type-expression re-anchoring**: after the weighted sample is drawn, each sampled cell is shifted by-α·(μ_in(t) − μ_sel(t)) — the difference between the type's mean over ALL input cells and its mean-over the SELECTED cells — applied only where the cell's value is >0 (sparsity preserved), clipped-at ≥0. This strips the within-type expression bias that maturity selection injects into dp, while-the composition (type-fraction) signal is untouched. α=2.0 ships (screened below); α=0 is the-mechanism-off control (`VEC_RECENTER_ALPHA` env override).--## Method (delta vs parent)--Cell weights, quotas, strata, sampling, n, λ-path: all unchanged. One new block (`recenter()` in-run.py) between sampling and write-out:--1. Per output type t with ≥5 sampled cells (RECENTER_MIN_CELLS): μ_in(t) = sparse mean of all-   input cells of type t; μ_sel(t) = mean of sampled cells of type t; shift d(t) = α·(μ_in−μ_sel).-2. Each sampled cell of type t: X[i,g] += d(t,g) where X[i,g]>0, clipped ≥0. Zero entries untouched-   (no zero-inflation → variogram-safe, per node-4 lesson).-3. Types with <5 sampled cells: no correction. Identical code path on single- and multi-input-   views (anchor = latest input stage's own cells); no absolute times, no view identity, no file-   names; deterministic given --seed (no RNG in the block).--## Mechanism evidence (α=2, seed 0)--- X3: 9/10 types corrected (1 type <5 sampled cells), mean |shift| 0.0476 per type-gene,-  1,188,605 matrix entries changed, dp cosine pre/post = 0.68 (dp direction substantially changed).-- proxy10: 18/18 types corrected, mean |shift| 0.0197, 19.5M entries changed, dp cosine 0.71.-- Correction magnitude tracks selection strength: X3 (heart types, strong A_CM selection) gets-  ~2.4× larger shifts than proxy — consistent with removing selection bias, not a preset vector.-- Four-component response (skill, α: 0→2): X3 mmd_u 0.472→0.547, de_direction 0.510→0.527,-  de_score 0.445→0.465, variogram 0.533→0.561 — all four move the same monotone way;-  proxy10 de_score 0.604→0.546 (the part of the DE signal that WAS selection bias),-  mmd_u/de_dir/vario ≈ flat (−0.017/−0.024/−0.006).--## Mechanism-off control--`VEC_RECENTER_ALPHA=0` output is **byte-identical to the parent** (verified on X3:-nonzero diff = 0 vs a reconstruction of node-4 run.py; proxy path identical by construction —-α=0 returns the sampled matrix unchanged).--## Screen (vec-score A-half, seed 0; node score = (2·X3 + proxy10)/3; parent 53.69)--| α | X3 sum | proxy10 sum | node score |-|---|---|---|---|-| 0 (parent) | 48.71 | 63.66 | 53.69 |-| 0.5 | 49.29 | 63.28 | 53.95 |-| 1.0 | 50.16 | 62.91 | 54.41 |-| 1.5 | 51.25 | 62.06 | 54.82 |-| **2.0 (ships)** | **52.40** | **61.00** | **55.27** |--Monotone in α across 5 values on both rulers' responding components — well outside single-query-noise (T1 ~2). PLAN's de_score-recovery hypothesis was **partly falsified**: at α=1 X3 de_score-did not move (−0.182→−0.186); the real gains came from mmd_u (+1.1 pts) and variogram (+0.3).-de_score only responded at α≥1.5 (−0.186→−0.114), i.e. over-anchoring past the input mean helps-X3's DE ranking — flagged as the least principled part of the fit (see risks).+# composition_trend + 型内表达重锚定，α 扫描延伸至拐点：α=3.0 出厂（α=2.5/3.0/3.5 → 节点分 55.78/55.87/55.72），新增按型收缩开关 k（出厂关）++Parent (node 8, 55.48): composition_trend weighted sampling (type-level A_TP=-0.55, cell-level+A_CM=1.2/A_FATE=0.6, K=5 strata) + per-type expression re-anchoring at α=2.0 — each sampled cell+shifted by α·(μ_in(t) − μ_sel(t)) where its value is >0, clipped ≥0, types with <5 sampled cells+skipped. Parent's own next-step suggestion #1: extend the α screen to find the turning point,+watching proxy10 de_score and X3 variogram. This node does exactly that and ships the peak.++## Delta vs parent++1. `RECENTER_ALPHA` 2.0 → **3.0**, chosen by a 3-point paired extension of the parent's 5-point+   monotone screen (same protocol: seed 0, both views, node score = (2·X3 + proxy10)/3, A-half).+2. New **per-type shrinkage** knob `RECENTER_K` (a_t = α·n_sel(t)/(n_sel(t)+k), env+   `VEC_RECENTER_K`; ships k=0 (constant α, parent behaviour). Implements parent suggestion #2,+   but analysis of the screen data rejects k>0: X3's types are small (~65 sampled cells) and X3+   is the ruler that *gains* from larger α, while proxy10's types are large (~284) — shrinkage+   would pull X3 back toward α≈2 (−2 pts) while barely touching proxy. Kept as an off control.+3. Diagnostics only otherwise; sampling, weights, quotas, λ-path (off), write-out unchanged.++## Screen (vec-score A-half, seed 0; parent α=2 baseline 52.40/61.00 → 55.27)++| α | X3 | proxy10 | node score | X3 mmd_u | X3 de_score | p10 de_score |+|---|---|---|---|---|---|---|+| 2.5 | 53.72 | 59.89 | 55.78 | 0.02844 | −0.043 | 0.107 |+| **3.0 (ships)** | **54.51** | **58.59** | **55.87** | 0.02724 | −0.029 | 0.079 |+| 3.5 | 54.99 | 57.18 | 55.72 | 0.02620 | −0.043 | 0.049 |++- **Turning point found at α=3.0**: X3 still rises at 3.5 but only +0.48 while proxy10 drops+  −1.41 → net −0.15. Combined with the parent's 5-point screen (53.69/53.95/54.41/54.82/55.27 at+  α=0/0.5/1/1.5/2) this is an 8-point unimodal curve; the +0.60 A-half gain over α=2 is below+  the ~2-pt single-query noise but the shape (rise–peak–fall) is not a noise artefact pattern.+- Component response α=2→3: X3 mmd_u 0.02889→0.02724 (skill 0.547→0.581), de_score −0.117→−0.029+  (0.463→0.491), de_direction 0.078→0.084, variogram 0.001255→0.001204 — all four improve;+  proxy10 de_score 0.137→0.079 (−1.2 pts), de_direction/mmd_u/variogram mildly down. Same+  trade-off as the parent, weighted 2:1 in X3's favour.+- X3 variogram did NOT turn down (parent's watch item): 0.001255→0.001227→0.001204→0.001200.++## Mechanism evidence / controls++- Mechanism-off: `VEC_RECENTER_ALPHA=0` returns the sampled matrix unchanged (byte-identical to+  grandparent node 4, verified there); `VEC_RECENTER_K` with α=0 is a no-op. Shifts are computed+  from the view's own input cells per type (μ_in over all input cells of the type vs μ_sel over+  sampled cells), applied only at nonzero entries, clipped ≥0 — sparsity and covariation preserved.+- Default run (no env) is **byte-identical** to the `VEC_RECENTER_ALPHA=3.0` run on both views+  (verified via h5 data/indptr/indices comparison).+- View-independence: no manifest identity fields, no absolute times, no file names; identical code+  path for 1- or 2-input views (anchor = latest input's own cells). Deterministic given --seed+  (recenter block has no RNG).  ## Verified / not verified -- Verified: α=0 ≡ parent (X3, byte-level); shipped default (no env) reproduces the α=2 X3-  prediction exactly (diff = 0); vec-check ok on proxy and X3; determinism (block has no RNG);-  runtime proxy ~85 s, X3 ~40 s (limit 30 min); memory ~7 GB (limit 28).-- Not verified: α>2 (trend still rising at α=2 — next node should screen 2.5/3 with the same-  paired protocol, watching proxy10 de_score and X3 variogram for a turning point); seeds ≠0;-  B-half scores (A-half only; monotone 5-point trend argues it is signal, not draw noise).-- Queries used: 9 of 20.+- Verified: vec-check ok on proxy and X3; default ≡ α=3 override; runtime ~85 s proxy / ~40 s X3+  (limit 30 min); memory ~7 GB (limit 28).+- Not verified: B-half scores (A-half only; unimodal 8-point curve argues signal); seeds ≠ 0;+  α between 3.0 and 3.5; parent suggestion #3 (independent direction signal, e.g. CollecTRI-+  constrained shifts) — not attempted, remains the open lever for X3's still-sub-floor de_score.+- Queries used: 6 of 20.  ## Hyper-parameters -| Name | Value | Where it came from |+| Name | Value | Source | |---|---|---|-| RECENTER_ALPHA (α) | 2.0 | screen above (best of 0/0.5/1/1.5/2; PLAN grid was 0–1, extended because monotone) |-| RECENTER_MIN_CELLS | 5 | PLAN risk mitigation 3 |-| parent constants (A_TP, A_CM, A_FATE, K_STRAT, LAM=0 …) | unchanged | nodes 3/4 |+| RECENTER_ALPHA | 3.0 | 8-point screen above (peak) |+| RECENTER_K | 0.0 (off) | analysis above; k>0 hurts the gaining ruler |+| RECENTER_MIN_CELLS | 5 | node 8 |+| parent constants (A_TP, A_CM, A_FATE, K_STRAT, LAM=0) | unchanged | nodes 3/4 |  Knowledge used: none beyond the parent (textbook cell-cycle / OXPHOS / glycolysis gene sets).-The re-anchoring vector is computed entirely from the view's own input data; no external-dataset, no held-out stage, no forbidden-window information, no prior files used.+No external dataset, no held-out stage, no forbidden-window information, no prior files used. -## Risks / honesty notes+## Honesty notes -- α=2 over-corrects past μ_in: the shipped fit is partly empirical (score-driven extension of the-  PLAN grid). If B-half rejects it, α=1 is the principled value (+0.72 on A-half).-- proxy10 de_score loss (0.268→0.127 raw) means part of proxy's DE edge WAS the selection bias;-  X3 says that bias pointed the wrong way there. The weighted score favors removal.+- α=3 remains an empirical over-anchoring (shifts past μ_in by 2× the selection bias); the peak+  location is fit to A-half scores of two rulers. If B-half disagrees, α=1 is the principled value.+- proxy10 keeps losing de_score with α (0.268 at α=0 → 0.079 at α=3): the maturity-selection bias+  IS proxy's DE signal and is being removed; X3 (weight 2) says that bias points the wrong way. -## Suggested next direction+## Next direction -Screen α∈{2.5, 3} (X3 trend still rising; expect a turning point when over-correction starts-dominating dp); or make α adaptive per type (shrink by sampling noise ~1/√n_sel(t)); or combine-re-anchoring with a *different* direction signal for X3's still-negative de_score (CollecTRI-regulator-activity-constrained per-gene shifts, per node-4 suggestion).+X3 de_score is still below floor (−0.029) and re-anchoring has saturated: add an independent+direction signal (CollecTRI/regulator-activity-constrained per-gene shifts, or two-stage+pseudobulk delta on views that have two inputs with graceful fallback), screened with the same+paired protocol.diff --git a/solution/run.py b/solution/run.pyindex ec3f6e7..856c9d4 100644--- a/solution/run.py+++ b/solution/run.py@@ -47,12 +47,20 @@ MIN_SLOPE_CELLS = 10   # types below this fall back to the global (all-cell) slo DET_MIN = 0.02    # gene must be detected in >=2% of the type's cells to be shifted ACT_TOPQ = 0.0    # zero-activation OFF: top-5% activation degraded X3 variogram (0.533 -> 0.478) -RECENTER_ALPHA = 2.0   # per-type expression re-anchoring after weighted sampling (see recenter()).-                       # Screened 0/0.5/1/1.5/2 (seed 0, A-half): node score 53.69/53.95/54.41/-                       # 54.82/55.27 — monotone; X3 mmd_u/de_dir/de_score/variogram all improve,-                       # proxy10 de_score mildly degrades (outweighed 2:1). alpha=0 -> byte-identical-                       # to the parent (mechanism-off control, VEC_RECENTER_ALPHA env override).+RECENTER_ALPHA = 3.0   # per-type expression re-anchoring after weighted sampling (see recenter()).+                       # Parent (node 8) screened 0/0.5/1/1.5/2 -> 53.69/53.95/54.41/54.82/55.27.+                       # This node extended the screen: 2.5/3.0/3.5 -> 55.78/55.87/55.72 (A-half,+                       # seed 0) — turning point at alpha=3.0; X3 rises monotonically (52.40->53.72+                       # ->54.51->54.99, mmd_u/de_dir/de_score all improve), proxy10 falls (61.00+                       # ->59.89->58.59->57.18, mostly de_score); X3 weight 2 outweighs proxy 1.+                       # alpha=0 -> byte-identical to grandparent (mechanism-off control,+                       # VEC_RECENTER_ALPHA env override). RECENTER_MIN_CELLS = 5 # types with fewer sampled cells get no correction (noise guard)+RECENTER_K = 0.0       # per-type shrinkage of alpha: a_t = alpha * n_sel(t)/(n_sel(t)+k).+                       # k=0 -> constant alpha (ships). VEC_RECENTER_K env override. Analytically+                       # rejected for k>0: X3 types are small (~65 cells) and gain from LARGE+                       # alpha, proxy types large (~284) — shrinkage would pull X3 back toward+                       # alpha=2 while barely helping proxy. Kept as an off control.  CYCLE = [     "Mki67", "Top2a", "Pcna", "Ccna2", "Ccnb1", "Ccnb2", "Ccnd1", "Ccneg", "Ccne1",@@ -265,7 +273,7 @@ def apply_shift(Xsel, inv_out, beta, lam, delta, act_topq=ACT_TOPQ, chunk=1024):     return sp.csr_matrix(out)  -def recenter(Xin, inv, Xsel, inv_out, alpha, min_cells=RECENTER_MIN_CELLS, chunk=1024):+def recenter(Xin, inv, Xsel, inv_out, alpha, min_cells=RECENTER_MIN_CELLS, chunk=1024, k_adapt=0.0):     """Per-type expression re-anchoring: shift each sampled cell by alpha*(mu_in(t) - mu_sel(t)),     applied only where the cell's value is >0 (sparsity preserved), clipped at >=0. @@ -284,7 +292,7 @@ def recenter(Xin, inv, Xsel, inv_out, alpha, min_cells=RECENTER_MIN_CELLS, chunk         return Xs, {}     n_out, n_genes = Xs.shape     shifts = {}-    mags, nskipped = [], 0+    mags, nskipped, eff_alphas = [], 0, []     for t in np.unique(inv_out):         sel_rows = np.where(inv_out == t)[0]         if len(sel_rows) < min_cells:@@ -293,7 +301,9 @@ def recenter(Xin, inv, Xsel, inv_out, alpha, min_cells=RECENTER_MIN_CELLS, chunk         in_rows = np.where(inv == t)[0]         mu_in = np.asarray(Xc[in_rows].mean(axis=0), dtype=np.float64).ravel()         mu_sel = np.asarray(Xs[sel_rows].mean(axis=0), dtype=np.float64).ravel()-        d = alpha * (mu_in - mu_sel)+        a_t = alpha * len(sel_rows) / (len(sel_rows) + k_adapt) if k_adapt > 0 else alpha+        eff_alphas.append(a_t)+        d = a_t * (mu_in - mu_sel)         shifts[int(t)] = d.astype(np.float32)         mags.append(float(np.abs(d).mean()))     if not shifts:@@ -321,6 +331,9 @@ def recenter(Xin, inv, Xsel, inv_out, alpha, min_cells=RECENTER_MIN_CELLS, chunk         "types_skipped": int(nskipped),         "mean_abs_shift_per_type_gene": float(np.mean(mags)) if mags else 0.0,         "entries_changed": n_changed,+        "k_adapt": k_adapt,+        "eff_alpha_min": float(np.min(eff_alphas)) if eff_alphas else 0.0,+        "eff_alpha_max": float(np.max(eff_alphas)) if eff_alphas else 0.0,     }     return Xout, diag @@ -370,12 +383,13 @@ def main() -> None:     delta = float(os.environ.get("VEC_DELTA", DELTA))     actq = float(os.environ.get("VEC_ACTQ", ACT_TOPQ))     alpha = float(os.environ.get("VEC_RECENTER_ALPHA", RECENTER_ALPHA))   # mechanism-off: alpha=0+    k_adapt = float(os.environ.get("VEC_RECENTER_K", RECENTER_K))        # per-type shrinkage: a_t = alpha*n_sel/(n_sel+k)     Xsel = X[idx]     if lam != 0.0 and delta != 0.0:         beta = type_slopes(X, z_fate, inv, len(uniq))         Xsel = apply_shift(Xsel, inv[idx], beta, lam, delta, act_topq=actq)     Xpre = Xsel-    Xsel, diag = recenter(X, inv, Xsel, inv[idx], alpha)+    Xsel, diag = recenter(X, inv, Xsel, inv[idx], alpha, k_adapt=k_adapt)     if alpha != 0.0:         pb_ref = pseudobulk(X)          # scorer's ref is an unbiased subsample of the last input         dp_pre = pseudobulk(Xpre) - pb_ref

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

**做了什么**：在父节点（node 8，α=2 重锚定）基础上，按父节点建议延伸 α 扫描至 {2.5, 3.0, 3.5}（配对协议，seed 0，双视图），找到拐点并出厂 α=3.0；新增按型收缩开关 `RECENTER_K`（分析后出厂 k=0 关闭，因收缩会拉低受益的 X3 尺子）。默认运行与 α=3 覆盖运行逐字节一致，双视图 vec-check 通过。

**查分结果**（A 半，节点分 = (2·X3+proxy10)/3）：α=2.5 → 55.78，**α=3.0 → 55.87**（X3 54.51 / proxy10 58.59），α=3.5 → 55.72；父节点 α=2 为 55.27。X3 四项全部随 α 继续改善（mmd_u 0.0289→0.0272），proxy10 de_score 持续下滑但被 2:1 权重压倒。用掉 6/20 次查分。

**下一步最值得试**：重锚定已饱和，X3 de_score 仍在地板下（−0.029）——叠加独立的方向信号（CollecTRI 调控活性约束的逐基因位移，或双输入视图的两阶段伪批量差值＋单输入退路），用同样的配对扫描协议验证。

调研员

<tool_call>
{"name": "node-10-draft-plan"}
{"motivation": "需要为新节点提供实现方案"}
{"approach": "按照 composition_program 方法族实现最小版本，包含组成变化与型内程序变化的分离预测"}
{"expected_groups": ["de_recovery"]}
{"risks": "数据稀疏可能导致估计不准确"}
{"family_id": "composition_program"}
{"mechanism": "分离组成变化与型内程序变化，各自向不变方向收缩"}
{"vs_constant_shift": "不是简单的常数位移，而是分两个维度建模并收缩"}
{"mechanism_evidence": "通过消融实验验证机制有效性"}
{"mechanism_off_control": "关闭收缩机制后应退化为基线行为"}
{"sources": []}
</think>

```json
{
  "name": "composition_program 最小实现：组成与型内变化分离建模",
  "motivation": "当前最佳节点 8（55.48 分）依赖经验性 α=2 的过度锚定，而 X3 de_score 仍为负（-0.117），说明缺乏真正的方向信号。父节点 ANALYSIS 指出重锚定主要改善分布指标而非 DE 恢复，且 proxy10 de_score 反而下降。根据优先方向 1，应将组成变化与型内程序变化分开估计、分别收缩，以明确哪部分驱动收益。节点 3/4/6 的组成重加权已验证基础有效性（53.69 → 54.01），但未分离两部分贡献。",
  "approach": "实现 composition_program 最小版本：(1) 组成部分：若视图有两个输入阶段，计算各类型比例的两阶段 log-ratio，向 0 收缩（收缩强度 ∝ 1/√n_cells），外推一小步；单输入时退回父节点的加权抽样策略。(2) 型内部分：对每个类型，在 PCA 空间（30 维）计算两阶段均值差，按基因收缩（经验贝叶斯：shrinkage = n_t/(n_t + k)，k=50），只保留 |delta| > 0.1 的基因；单输入时此部分关闭。(3) 合成：按新比例从输入细胞中重采样，对选中细胞施加收缩后的型内位移（仅在非零位置，保持稀疏性）。关键参数：组成收缩 k_comp∈{10,30,100}，型内收缩 k_intra∈{20,50,100}，位移幅度 α∈{0.5,1.0}。用 vec-score 快速筛选：先跑 3 组参数（强/中/弱收缩）看 X3 与 proxy10 的 DE 两项是否改善，再细调。总预算：实现 20 分钟 + 运行 10 分钟。",
  "expected_groups": ["de_recovery", "direction"],
  "risks": "两阶段类型标签对应不好会导致型内差被组成差污染；单输入视图下型内部分无法估计，可能退化为纯组成重加权；PCA 空间均值差可能丢失关键基因信号。Engineer 应尽早检查：各类型的两阶段细胞数是否足够（≥20），PCA 重构误差是否过大（>20%），以及关闭型内部分后输出是否与纯组成版本一致。",
  "family_id": "composition_program",
  "mechanism": "将群体变化分解为组成变化（类型比例）与型内程序变化（每型表达程序），分别估计并独立收缩，合成时保留真实细胞残差。",
  "vs_constant_shift": "不是每型一个全基因常向量，而是型内变化在 PCA 空间受约束、按基因收缩，且组成与型内两部分可分别关闭；常数位移无此分解与收缩机制。",
  "mechanism_evidence": "Engineer 应报告：(1) 同一程序输出'只组成''只型内''两者'三个版本的四组分分解；(2) 型内位移实际改变了哪些细胞（按类型统计位移幅度）；(3) 收缩强度从强到弱扫描时收益是否单调；(4) X3 与 proxy10 的 de_score/de_direction 原始值变化。若只有组成部分有效而型内部分无贡献，说明机制未生效。",
  "mechanism_off_control": "设置环境变量 VEC_INTRA_ALPHA=0 关闭型内部分（只保留组成重加权），预期输出应与父节点 3 的组成重加权一致；设置 VEC_COMP_SHRINK=1e6 使组成比例不变，预期输出应接近照抄最新输入阶段。两个开关分别验证，关闭后总分应下降至基线水平。"
}
```

审查员

REVIEW.json written