总览 · ← 返回运行 20261002-034201-search-t1-abc-r1-C-native

节点 n58 终选程序?按该运行锁定的规则最终选出的程序；可能是候选节点，也可能由护栏回退到基线。在终选来历上

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 cff260e873: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 14056cdde8。改动的文件：solution/METHOD.md +2 −2、solution/run.py +29 −6

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 876a784..032c2b3 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,4 +1,4 @@ ## 改了什么-相对父节点（节点 20，分层无放回抽样 + EB 收缩位移 alpha=0.03）：(1) 完全移除逐基因 EB 收缩位移，因父节点 ANALYSIS 明确建议消融且 covariation 下降；(2) 新增基于距离的分层抽样，N_BINS=20（比 Attempt 1 的 12 更细），MIN_CELLS_FOR_BINNING=30（避免小类型不可靠分箱）；(3) 家族权重 heart=1.5/reduced=0.2/gut=0.95，与全树最佳 Attempt 1 一致。第 0 轮用 1.6/0.15/0.9 得 51.96（proxy 52.97）；第 1 轮改权重得 52.20（proxy 53.33，covariation 51.9）；本轮增加分箱数从 12 到 20 以提升 covariation（Attempt 1 得 52.55 用的 12 bins，更细分箱应更好保留径向分布）。+相对父节点（节点 40，分箱分层抽样 + 家族权重 1.5/0.2/0.95）：(1) 用稀疏矩阵公式 ||x||² - 2x·c + ||c||² 计算到质心距离，替代 todense()，降低内存峰值（ANALYSIS 建议）；(2) 在分箱内引入密度加权抽样（DENSITY_POWER=0.5）：距质心近的细胞被抽样概率更高，保留类型核心表达模式，目标提升 cell_state 分组（当前最弱 51.37）。分箱配额不变，只改变箱内的抽样概率。 ## 用到的知识与出处-实验表 Attempt 1（score 52.4971，covariation 52.55，N_BINS=12）；父节点 ANALYSIS（EB 收缩导致 covariation 下降）；方向库 T1-01（组成重加权）；方法卡（位移在 proxy 无法验证）。+父节点 ANALYSIS next_suggestions（稀疏距离计算）；方向库 T1-01（组成重加权）；T1 噪声约 2 分，单次查询差距小于 2 不算进步。diff --git a/solution/run.py b/solution/run.pyindex 5b68ce8..c506ea3 100644--- a/solution/run.py+++ b/solution/run.py@@ -29,6 +29,8 @@ from src.task1_temporal.view_io import (  N_BINS = 20 MIN_CELLS_FOR_BINNING = 30+MIN_CELLS_FOR_SPARSE_DIST = 200+DENSITY_POWER = 0.5  _HEART_PAT = re.compile(     r"cm\b|cardi|heart|shf|endocard|oft\b|avc|ift\b|sv[- ]cm|v[- ]cm|"@@ -58,6 +60,20 @@ def family_weight(name: str) -> float:   +def _sparse_distances(X_sub, centroid):+    """Compute squared distances from rows of sparse X_sub to centroid without densifying."""+    if sp.issparse(X_sub):+        X_sq = np.asarray(X_sub.multiply(X_sub).sum(axis=1)).ravel()+        dot_c = np.asarray(X_sub.dot(centroid)).ravel()+        c_sq = float(centroid @ centroid)+        dists_sq = X_sq - 2.0 * dot_c + c_sq+        np.maximum(dists_sq, 0.0, out=dists_sq)+        return np.sqrt(dists_sq)+    else:+        diff = np.asarray(X_sub) - centroid+        return np.linalg.norm(diff, axis=1)++ def main() -> None:     parser = argparse.ArgumentParser()     parser.add_argument("--data", required=True)@@ -100,11 +116,10 @@ def main() -> None:         if len(type_idx) > MIN_CELLS_FOR_BINNING and n_take >= 5:             sub = last.X[type_idx]             if sp.issparse(sub):-                sub = np.asarray(sub.todense())+                centroid = np.asarray(sub.mean(axis=0)).ravel()             else:-                sub = np.asarray(sub)-            centroid = sub.mean(axis=0)-            dists = np.linalg.norm(sub - centroid, axis=1)+                centroid = np.asarray(sub).mean(axis=0).ravel()+            dists = _sparse_distances(sub, centroid)             bin_edges = np.quantile(dists, np.linspace(0, 1, N_BINS + 1)[1:-1])             bin_ids = np.searchsorted(bin_edges, dists)             bin_props = np.bincount(bin_ids, minlength=N_BINS) / len(type_idx)@@ -116,10 +131,18 @@ def main() -> None:             per_bin = np.minimum(per_bin, np.bincount(bin_ids, minlength=N_BINS))             chosen = []             for b in range(N_BINS):-                b_idx = type_idx[bin_ids == b]+                b_mask = bin_ids == b+                b_idx = type_idx[b_mask]                 nb = min(per_bin[b], len(b_idx))                 if nb > 0:-                    chosen.append(rng.choice(b_idx, size=nb, replace=False))+                    if nb == len(b_idx):+                        chosen.append(b_idx)+                    else:+                        b_dists = dists[b_mask]+                        inv_d = 1.0 / (b_dists + 1e-6)+                        w = inv_d ** DENSITY_POWER+                        w /= w.sum()+                        chosen.append(rng.choice(b_idx, size=nb, replace=False, p=w))             if chosen:                 sel = np.concatenate(chosen)             else:

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

审查员

REVIEW.json written

stream

I'll start by reading the full PROMPT.md to understand the complete requirements, and also check the solution directory.

<tool_call>
{"name": "Read", "arguments": {"file_path": "/home/spark-longxinyang/.local/state/vec-report/calls/earch-t1-abc-r1-C-native-n58-41bae4/work/PROMPT.md", "offset": 664}}
</tool_call>
<tool_call>
{"name": "Bash", "arguments": {"command": "ls /home/spark-longxinyang/.local/state/vec-report/calls/earch-t1-abc-r1-C-native-n58-41bae4/work/solution/ 2>/dev/null || echo \"no solution dir\"", "description": "List solution directory contents"}}
</tool_call>