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节点 n57

改了什么

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 c2c3fc3602: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 a00b55ccea。改动的文件：solution/METHOD.md +8 −8、solution/run.py +76 −19

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 57dc8d9..72d550c 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,11 +1,11 @@ ## 改了什么-相对父节点（node 17，score 48.94）的全部改动：-1. 加入 sign disagreement detection：SNR 加权方向一致性检测，与主导方向相反的基因额外收缩。相比第 0 轮（factor=0.80, threshold=0.22），本轮大幅放宽为 factor=0.92, threshold=0.35，减少对 DE 基因的过度压制（第 0 轮 de_recovery 从 49.15 降到 48.70，X3 从 46.74 降到 46.50）。-2. GENE_SHRINK_POWER 从 2.0 改为 2.5，GENE_SHRINK_K 从 2.5 改为 3.0（采用 Program 1 参数），更激进地收缩低 SNR 基因，提升 covariation 和 X3。-3. SNR 显著性阈值从 2.0 降到 1.5，ALPHA_MAX 从 0.75 提到 0.80。-4. 保留 include_external=False 过滤、单阶段退化为 copy_last、输出前 clip≥0。-理由：Program 1（power=2.5, K=3.0, 无 sign disagreement）在 X3 上得 47.21、covariation 47.94，均优于第 0 轮（46.50、47.69）。本轮采用 Program 1 的收缩参数，仅保留极温和的 sign disagreement（0.92/0.35），期望兼顾 cell_state 增益与 de_recovery/X3 恢复。+相对父节点（node 22，score 49.10）的全部改动：+1. 加入单阶段退化时的组成重加权（cardiac family weights：CARDIAC_W=1.4, DOWNWEIGHT_W=0.4, DROP_W=0.05），使 proxy/proxy2 视图不再退化为纯 copy_last，直接对标 Program 1（node 43，score 53.34）的核心机制。+2. 加入 per-cell rank-based projection scaling（CELL_SCALE_MAX=1.4, CELL_SCALE_MIN=0.6）：按细胞在 delta 方向上的投影排名缩放位移幅度，保留群体内协方差结构，提升 covariation 和 direction 分数。+3. 参数调整：ALPHA_MAX 0.80→0.85，GENE_SHRINK_POWER 2.5→2.0（对齐 Program 1 配置）。+4. 完全移除 sign disagreement 机制（SIGN_DISAGREE_FACTOR/THRESHOLD）：父节点分析表明该机制在温和参数下近似无效，在激进参数下伤 de_recovery，移除以恢复 de_recovery。+理由：Program 1（node 43）在相同框架下得 53.34，其核心差异是组成重加权 + rank-based scaling + 无 sign disagreement。本节点直接采用这些已验证有效的组件。 ## 用到的知识与出处+- 实验表 Program 1（node 43，score 53.34）：验证了组成重加权 + rank-based projection scaling + ALPHA_MAX=0.85 + GENE_SHRINK_POWER=2.0 的效果 - 方法卡 k018（Damped per-type shift: shrinkage alpha on the observed delta）-- 实验表 Program 1（node 19，score 49.18）：验证了 power=2.5, K=3.0, SNR_SIGNIFICANT_THRESHOLD=1.5, ALPHA_MAX=0.80 的效果-- 第 0 轮反馈：sign disagreement factor=0.80 导致 de_recovery -0.45、X3 -0.24，需放宽+- 父节点分析：sign disagreement 在温和参数下无效、激进参数下伤 de_recovery，建议移除diff --git a/solution/run.py b/solution/run.pyindex f824743..a428883 100644--- a/solution/run.py+++ b/solution/run.py@@ -21,19 +21,43 @@ from src.task1_temporal.view_io import (     load_manifest,     panel_genes,     read_stage,-    sample_rows,     target_n_cells,     write_prediction, ) -ALPHA_MAX = 0.80+ALPHA_MAX = 0.85 ALPHA_MIN = 0.1 SIGN_THRESHOLD = 0.05-GENE_SHRINK_POWER = 2.5+GENE_SHRINK_POWER = 2.0 GENE_SHRINK_K = 3.0-SIGN_DISAGREE_FACTOR = 0.92-SIGN_DISAGREE_THRESHOLD = 0.35 SNR_SIGNIFICANT_THRESHOLD = 1.5+CELL_SCALE_MAX = 1.4+CELL_SCALE_MIN = 0.6++CARDIAC_SUBSTRINGS = [+    "CM", "SHF", "Endocardium", "BEC", "Proepicardium",+    "Pericardium", "PHM", "JCF", "NCC", "OFT", "AVC", "IFT", "SV",+]+DOWNWEIGHT_SUBSTRINGS = ["Surface Ectoderm", "EXEM", "Paraxial Mesoderm"]+DROP_SUBSTRINGS = ["Neural Tube"]++CARDIAC_W = 1.4+DOWNWEIGHT_W = 0.4+DROP_W = 0.05+DEFAULT_W = 1.0+++def family_weight(name: str) -> float:+    for s in DROP_SUBSTRINGS:+        if s in name:+            return DROP_W+    for s in DOWNWEIGHT_SUBSTRINGS:+        if s in name:+            return DOWNWEIGHT_W+    for s in CARDIAC_SUBSTRINGS:+        if s in name:+            return CARDIAC_W+    return DEFAULT_W   def _to_dense(X):@@ -58,12 +82,28 @@ def main() -> None:      last = read_stage(args.data, stages[-1], genes)     rng = np.random.default_rng(args.seed)-    rows = sample_rows(last.n_obs, target_n_cells(manifest, last.n_obs), rng)+    n_target = target_n_cells(manifest, last.n_obs)+    labels_last = labels_of(last)++    if len(stages) >= 2:+        if n_target <= last.n_obs:+            rows = rng.choice(last.n_obs, size=n_target, replace=False)+        else:+            rows = rng.choice(last.n_obs, size=n_target, replace=True)+    else:+        weights = np.array([family_weight(l) for l in labels_last], dtype=np.float64)+        w_sum = weights.sum()+        if w_sum > 0:+            weights /= w_sum+        else:+            weights = np.ones(last.n_obs, dtype=np.float64) / last.n_obs+        replace = n_target > last.n_obs+        rows = rng.choice(last.n_obs, size=n_target, replace=replace, p=weights)+     X = _to_dense(last.X[rows])      if len(stages) >= 2:         prev = read_stage(args.data, stages[-2], genes)-        labels_last = labels_of(last)         labels_prev = labels_of(prev)          Xd_last = _to_dense(last.X)@@ -72,9 +112,10 @@ def main() -> None:         types_prev_set = set(np.unique(labels_prev))         labels_sampled = labels_last[rows] -        for t in np.unique(labels_sampled):-            mask_s = labels_sampled == t+        deltas = {}+        for t in np.unique(labels_last):             if t not in types_prev_set:+                deltas[t] = np.zeros(len(genes), dtype=np.float32)                 continue             mask_last = labels_last == t             mask_prev = labels_prev == t@@ -102,16 +143,32 @@ def main() -> None:                     frac_significant / 0.3, 1.0                 )                 gene_shrink = (snr / (snr + GENE_SHRINK_K)) ** GENE_SHRINK_POWER-                weighted_sign = np.sum(np.sign(raw_delta) * snr)-                total_snr = snr.sum() + 1e-12-                if abs(weighted_sign) > SIGN_DISAGREE_THRESHOLD * total_snr:-                    dominant_sign = np.sign(weighted_sign)-                    disagree = np.sign(raw_delta) != dominant_sign-                    gene_shrink = gene_shrink.copy()-                    gene_shrink[disagree] *= SIGN_DISAGREE_FACTOR--            delta = (raw_delta * alpha * gene_shrink).astype(np.float32)-            X[mask_s] += delta[np.newaxis, :]++            deltas[t] = (raw_delta * alpha * gene_shrink).astype(np.float32)++        for t in np.unique(labels_sampled):+            mask_s = labels_sampled == t+            if t not in deltas:+                continue+            delta_t = deltas[t]+            nonzero_genes = np.abs(delta_t) > 1e-8+            if nonzero_genes.sum() > 1:+                centroid = Xd_last[labels_last == t][:, nonzero_genes].mean(axis=0)+                proj_dir = delta_t[nonzero_genes]+                proj_norm = np.linalg.norm(proj_dir) + 1e-12+                proj_dir = proj_dir / proj_norm+                cell_sub = X[mask_s][:, nonzero_genes]+                centered = cell_sub - centroid[np.newaxis, :]+                proj_scores = centered @ proj_dir+                ranks = np.argsort(np.argsort(proj_scores)).astype(np.float64)+                norm_proj = ranks / max(len(ranks) - 1, 1)+                scale = CELL_SCALE_MIN + (CELL_SCALE_MAX - CELL_SCALE_MIN) * norm_proj+                X[mask_s][:, nonzero_genes] += delta_t[nonzero_genes][np.newaxis, :] * scale[:, np.newaxis]+                zero_genes = ~nonzero_genes+                if zero_genes.any():+                    X[mask_s][:, zero_genes] += delta_t[zero_genes][np.newaxis, :]+            else:+                X[mask_s] += delta_t[np.newaxis, :]          del prev, Xd_last, Xd_prev 

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）。

没有记录调研来源。

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

native

stream

Let me read the rest of the PROMPT.md and the current program.

<tool_call>
<function=Read>
<parameter=file_path>
/home/spark-longxinyang/.local/state/vec-report/calls/earch-t1-abc-r0-C-native-n57-c023a1/work/PROMPT.md
</parameter>
<parameter=offset>
680
</parameter>
</function>
</tool_call>
<tool_call>
<function=Glob>
<parameter=pattern>
solution/**
</parameter>
<parameter=path>
/home/spark-longxinyang/.local/state/vec-report/calls/earch-t1-abc-r0-C-native-n57-c023a1/work
</parameter>
</function>
</tool_call>