总览 · ← 返回运行 20261003-093415-search-t1-r2-D-s1

节点 n13

manifold_ode v2：ODE 速度范数只做型内细胞选择（不再位移表达），叠加两阶段类型频率趋势重加权；单输入退路=心脏解剖组成先验重加权+型内增殖程序(λ=0.15)。

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 3207dea9bb: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 1ec6cca39c。改动的文件：solution/METHOD.md +23 −23、solution/run.py +202 −63

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 4a494bc..f149c74 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,29 +1,29 @@-manifold_ode：PCA(25维,HVG2500)潜空间非自治神经ODE，Sinkhorn拟合相邻输入阶段，kNN流形惩罚，单步阻尼外推(α=0.3)+残差解码；单输入阶段回退为末阶段均匀复制。+manifold_ode v2：ODE 速度范数只做型内细胞选择（不再位移表达），叠加两阶段类型频率趋势重加权；单输入退路=心脏解剖组成先验重加权+型内增殖程序(λ=0.15)。 -## 方法（family_id: manifold_ode，按 PLAN 实现）-- 输入 ≥2 阶段时：取最后两个输入阶段，各采样 ≤2500 细胞，HVG(2500，按合并方差) + sklearn PCA(25)。-- 场 f(z,t)：MLP 26→48→48→25，tanh；t∈[0,1] 作为额外输入（非自治）。5 步 Euler 固定积分。-- 损失：geomloss SamplesLoss(sinkhorn, blur=0.05) 匹配前一阶段推后的潜位置到末阶段潜位置；λ_v=0.05 速度正则；λ_m=0.1 kNN(k=8) 流形惩罚，r=1.5×训练潜点 kNN 距离中位数，在积分第 2/3/4 步施加。Adam lr=1e-3，batch=512，≤200 epochs / 120s，50 epoch 无改善 early stop。-- 外推：对末阶段全部选中细胞，从 z_last 应用一次单位流（n_units=1），阻尼 α=0.3：z_pred = z_last + α(z_out − z_last)。PLAN 的 n_units=round(Δt目标/Δt训练)、α=0.6 在 X3 上更差（见下），故默认改为 1 步、α=0.3。-- 解码：x_out = x_in + (z_pred−z_last)@components（等价于 PLAN 的线性解码+逐细胞残差加回），限 HVG 列；非 HVG 列原样复制输入细胞；clip≥0，稀疏输出。-- 单输入阶段（proxy10）：无法训练 ODE，回退为末阶段均匀复制（等同 copy_last），符合 PLAN 第 7 步。-- 细胞数 = clip(末阶段细胞数, min_cells, max_cells)，rng(seed) 确定抽样。视图无关：只用时间差比值 (t_target−t_last)/(t_last−t_prev)，对时间平移不变；不读视图路径/名称。+## 方法（family_id: manifold_ode，按 PLAN Fix1/Fix2 实现后按查分证据修正）+### 两输入路径（X3、final）+1. ODE 训练与节点 11 相同：HVG(2500)+PCA(25) 潜空间，MLP 26→48→48→25，Sinkhorn(blur=0.05) 匹配相邻输入阶段，λ_v=0.05，kNN(k=8) 流形惩罚 λ_m=0.1，≤200 epochs/120s。+2. **组成（型间）**：按两阶段类型频率趋势外推 w_t = c1_t · clip((c1_t+5)/(c0_t+5), 0.2, 5)^T，T=(t_target−t_last)/(t_last−t_prev) 并截断 ≤2；largest_remainder 分配 n_out。纯数据驱动，无硬编码类型名。+3. **机制（型内，ODE 速度）**：对末阶段全部细胞算 v_i=f(z_i,t=1)，型内按 w_i=1+γ·clip(||v_i||/型内中位数,0.2,3)−1（γ=0.5）做无放回加权抽样（Gumbel top-k，seed 确定）——速度大的细胞（更动态/更晚成熟）被优先选中。**表达不做任何位移**（α=0 默认；PLAN 的 rank-K 速度投影位移已实现并用查分否决，见下）。 -## 对照与机制证据（X3 视图，seed 0，A 半）-- 机制开（MANIFOLD_OFF 未设，n_units=2, α=0.6）：X3 = 45.47（covariation 36.0）。-- 机制关（MANIFOLD_OFF=1，λ_m=0，其余相同）：X3 = 41.67（covariation 30.19）。流形惩罚生效：+3.8 总分，covariation +5.8，说明 kNN 约束确实保护了共变结构、改变了输出（非恒等）。-- ODE_OFF=1 标志走绝对基线（均匀复制）。-- α/n_units 扫描（X3）：u2/α0.6=45.47；u1/α0.7=42.79；u1/α0.3=46.63。α 越大越差 → 外推方向在 X3 上系统性偏差。+### 单输入路径（proxy10）+无法训练 ODE，继承节点 3/7 已验证策略（父节点教训：新家族在某视图无法运作时应继承父策略而非裸复制）：+1. `src.task1_temporal.reweight` 的解剖组成先验（心脏类型×1.6、边缘外胚层等×0.25、Neural Tube 剔除）重抽样 n_out 个真实细胞；+2. 型内表达程序 x_out = clip(x + λ·(x − mean_type)·z, 0)，λ=0.15，z=型内增殖评分（16 个细胞周期标记基因均值，通用知识：Mki67/Top2a/Ccnb1/Cdk1 等）的归一化秩×2（均值≈1），即高增殖（更晚期）细胞被推离型均值更多——伪时间梯度替代 ODE 速度的退化形式。 -## 查分结果-- proxy10（回退=copy_last）：52.97（de_recovery 51.5 / direction 58.7 / cell_state 51.7 / covariation 49.6）。-- X3 最佳 ODE 配置（u1, α0.3）：46.63，仍低于 X3 上 copy_last（节点1：47.92）。-- 结论（如实报告）：manifold_ode 在 X3 外推检验上不敌复制基线；de_score 为负说明学到的位移方向与真值部分反号。流形约束相对无约束是正贡献，但不足以弥补。+## 对照与机制证据（X3，seed 0，A 半）+- 机制开（默认）：X3 = 52.37（de_score +0.100，mmd 得分 16.33）。+- MECH_OFF=1（跳过型内速度加权→均匀抽样，λ_m=0，无位移；型间趋势重加权保留）：X3 = 51.32（de_score +0.086，mmd 15.76）。**机制净贡献 +1.05**，主要落在 cell_state(mmd) 与 de 两项；速度加权确实改变了被选中的细胞集合（输出逐元素不同）。+- 位移机制被否决的证据：rank-5 速度投影位移 α=0.2 → X3 45.82（de_score −0.243、variogram 0.00203）；纯速度范数全局重加权（无位移、无趋势重加权）→ 46.88（de_score −0.286）。说明 ODE 位移方向在 X3 上仍系统性押反，全局速度重加权也押错；把速度只用于**型内**排序后 de_score 首次转正（−0.22→+0.10）。+- proxy10 上被否决的变体：型内增殖加权抽样（maturity selection）+λ0.15 → 55.15，比均匀抽样+λ0.15 的 59.76 差 4.6（variogram 0.00103→0.00150），已回退。λ 扫描：0 → 59.04，0.15 → 59.76，0.3 → 58.96。++## 查分结果（A 半，seed 0）+- proxy10 = 59.76（父 52.75，de_direction 0.277、mmd 0.0288）；X3 = 52.37（父 45.41，copy_last 47.92）。估算节点分 (59.76+2×52.37)/3 ≈ 54.8（父 47.86）。  ## 已验证 / 未验证-- 已验证：两视图跑通、vec-check 通过、seed 确定、机制对照差异显著、X3 四组分分解。-- 未验证：final 视图（E8.5+E9.5→E10.5，T=1）上的表现；α>0.7 或多单位外推组合；λ_m 细扫（0.02–0.3 只用了 0.1）；PCA 维数/HVG 数敏感性。-- 知识来源：无保留阶段/禁窗数据；只用视图内输入数据。方法学参考 PLAN（k032：单间隔弱辨识，需非自治/阻尼）。+- 已验证：两视图跑通、vec-check 通过、seed 确定（Gumbel/rng 全部走 default_rng(seed)，torch.manual_seed(seed)）、MECH_OFF 对照、λ/α/γ 部分扫描。视图无关：只用时间差比值 T 与视图内数据，无绝对时间、无路径/视图名判断。+- 未验证：final（E8.5+E9.5→E10.5，T=1，此时趋势外推更温和）；γ、T 截断细扫；rank-K 位移在 final 上是否不同；proxy2 视图。+- 知识来源：细胞周期标记基因为通用增殖知识（非禁窗测量）；心脏解剖组成先验来自 harness 提供的 `src.task1_temporal.reweight` 快照（官方种子方法）；未用任何保留阶段/禁窗数据。 -## 下一步建议-X3 显示潜空间外推方向不可靠：可尝试把位移限制在少量高置信 PCA 程序方向、或仅对组成/型内选择做外推（如节点 3/7 家族），ODE 只用于型内成熟度排序而非直接位移。+## 下一步+1) proxy10 的 λ 程序输出稠密、损伤 variogram：可把程序限制在 HVG 或保持稀疏（只对非零元素位移）。2) X3 型间趋势的 T 截断与平滑 s 可细扫；3) 型内速度选择可换为速度在型内秩（对尺度更稳健）。diff --git a/solution/run.py b/solution/run.pyindex 9d5b9ff..27de9fa 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,6 +1,16 @@ #!/usr/bin/env python-"""manifold_ode: PCA-latent non-autonomous neural ODE, Sinkhorn fit + kNN manifold-penalty, damped extrapolation. Single-input fallback: uniform copy of last stage."""+"""manifold_ode v2: ODE velocity field on kNN-constrained PCA latent.++Two-input path: train ODE (Sinkhorn + manifold penalty) as node 11, then+  (a) rank-K velocity projection back-projected to gene space as a sparse+      per-cell shift (replaces full PCA decode),+  (b) velocity-norm reweighted sampling of last-stage cells (composition).+Single-input path: anatomical composition reweighting (src reweight prior)+  + within-type expression program x + lam*(x - mean_type)*z_fate, where+  z_fate is a proliferation-signature pseudotime proxy (degenerate+  velocity: pseudotime gradient substitutes for ODE velocity).+MECH_OFF=1 disables both mechanisms (uniform copy of last stage).+""" import argparse import json import os@@ -13,6 +23,11 @@ import torch import torch.nn as nn from geomloss import SamplesLoss +PROLIF_MARKERS = [+    "Mki67", "Top2a", "Ccnb1", "Cdk1", "Birc5", "Aurkb", "Ccna2", "Pcna",+    "Mcm2", "Mcm5", "Rrm2", "Tyms", "Cdc20", "Ube2c", "Kpna2", "Hmgb2",+]+  def load_genes(view):     with open(os.path.join(view, "genes.txt")) as f:@@ -47,56 +62,91 @@ def write_out(path, X, genes):     a.write(path)  -def main():-    p = argparse.ArgumentParser()-    p.add_argument("--data", required=True)-    p.add_argument("--out", required=True)-    p.add_argument("--seed", type=int, required=True)-    args = p.parse_args()+def weighted_sample(n_pool, n_out, w, rng):+    """Weighted sampling without replacement (Gumbel top-k), or with if pool small."""+    if n_pool <= n_out:+        p = w / w.sum()+        return rng.choice(n_pool, size=n_out, replace=True, p=p)+    keys = rng.gumbel(size=n_pool) + np.log(np.maximum(w, 1e-12))+    idx = np.argsort(-keys)[:n_out]+    idx.sort()+    return idx -    seed = args.seed-    rng = np.random.default_rng(seed)-    torch.manual_seed(seed) -    manifold_off = os.environ.get("MANIFOLD_OFF", "0") == "1"-    ode_off = os.environ.get("ODE_OFF", "0") == "1"+# ---------------------------------------------------------------- single input+def single_input_path(last, n_out, genes, rng, mech_off, lam):+    labels = last.obs["celltype"].astype(str).to_numpy()+    X = last.X -    view = args.data-    genes = load_genes(view)-    with open(os.path.join(view, "manifest.json")) as f:-        man = json.load(f)-    inputs = sorted(man["inputs"], key=lambda e: e["time"])-    min_c, max_c = man["min_cells"], man["max_cells"]-    t_target = man["target"]["time"]+    if mech_off:+        return uniform_copy(last, n_out, rng) -    last = read_input(view, inputs[-1])-    n_out = int(np.clip(last.n_obs, min_c, max_c))+    # --- composition reweighting (anatomical prior from src snapshot) ---+    from src.task1_temporal.reweight import (+        DROP_TYPES, type_weights, largest_remainder,+    )+    types = [t for t in np.unique(labels).tolist() if t not in DROP_TYPES]+    counts = np.array([(labels == t).sum() for t in types], dtype=np.float64)+    alloc = largest_remainder(counts * type_weights(types, 1.6, 0.25), n_out) -    if ode_off or len(inputs) < 2:-        X = uniform_copy(last, n_out, rng)-        write_out(args.out, X, genes)-        return+    # --- proliferation pseudotime proxy on full stage (for maturity selection) ---+    var_arr = np.asarray(last.var_names)+    pos = {g: i for i, g in enumerate(var_arr)}+    cols = [pos[g] for g in PROLIF_MARKERS if g in pos]+    Xd_all = X.toarray()+    if cols:+        prolif_all = Xd_all[:, cols].mean(axis=1)+    else:+        prolif_all = Xd_all.sum(axis=1)++    sel_idx, sel_type = [], []+    for t, n in zip(types, alloc):+        if n <= 0:+            continue+        pool = np.flatnonzero(labels == t)+        n = int(n)+        ch = rng.choice(pool, size=n, replace=pool.size < n)+        sel_idx.append(ch)+        sel_type.append(np.full(len(ch), t))+    sel_idx = np.concatenate(sel_idx)+    sel_type = np.concatenate(sel_type)++    Xsel = X[sel_idx].astype(np.float32)+    prolif = prolif_all[sel_idx]++    # --- within-type expression program ---+    Xd = Xsel.toarray()+    out = Xd.copy()+    for t in np.unique(sel_type):+        m = sel_type == t+        mt = Xd[m].mean(axis=0)+        n_t = int(m.sum())+        if n_t >= 10:+            r = np.argsort(np.argsort(prolif[m]))+            z = 2.0 * (r + 1) / (n_t + 1)  # mean ~1, high-prolif = advanced+        else:+            z = np.ones(n_t)+        out[m] = Xd[m] + lam * (Xd[m] - mt) * z[:, None]+    np.clip(out, 0.0, None, out=out)+    return sp.csr_matrix(out.astype(np.float32)) -    prev = read_input(view, inputs[-2])-    t_prev, t_last = inputs[-2]["time"], inputs[-1]["time"]-    T = (t_target - t_last) / max(t_last - t_prev, 1e-6)-    n_units = max(1, int(round(T)))-    n_units = int(os.environ.get("NUNITS", "1"))-    alpha = float(os.environ.get("ALPHA", "0.3")) -    # ---- subsample for training ----+# ----------------------------------------------------------------- two inputs+def two_input_path(prev, last, n_out, seed, rng, mech_off,+                   alpha, K, gamma, T_raw):     n_tr = 2500+     def sub(a, n):         if a.n_obs <= n:             return np.arange(a.n_obs)         idx = rng.choice(a.n_obs, size=n, replace=False)         idx.sort()         return idx+     i0, i1 = sub(prev, n_tr), sub(last, n_tr)     X0 = prev.X[i0].astype(np.float32).toarray()     X1 = last.X[i1].astype(np.float32).toarray() -    # ---- HVG selection on pooled training cells ----     Xp = np.concatenate([X0, X1], axis=0)     var = Xp.var(axis=0)     n_hvg = 2500@@ -105,22 +155,18 @@ def main():     X0h, X1h = Xp[: len(X0)][:, hvg], Xp[len(X0):][:, hvg]     del Xp -    # ---- PCA ----     from sklearn.decomposition import PCA     pca = PCA(n_components=25, random_state=seed)     pca.fit(np.concatenate([X0h, X1h], axis=0))-    comp = pca.components_.astype(np.float32)   # (25, n_hvg)+    comp = pca.components_.astype(np.float32)     mean = pca.mean_.astype(np.float32)      def encode(X):         return (X - mean) @ comp.T-    def decode(Z):-        return Z @ comp + mean      z0 = torch.from_numpy(encode(X0h))     z1 = torch.from_numpy(encode(X1h)) -    # ---- kNN manifold radius from training latents ----     Z = torch.cat([z0, z1], dim=0)     k = 8     with torch.no_grad():@@ -128,11 +174,11 @@ def main():         D.fill_diagonal_(float("inf"))         knn_d = D.topk(k, dim=1, largest=False).values[:, -1]         r = 1.5 * knn_d.median()-    lam_m = 0.0 if manifold_off else 0.1+    lam_m = 0.0 if mech_off else 0.1     lam_v = 0.05 -    # ---- ODE net -----    net = nn.Sequential(nn.Linear(26, 48), nn.Tanh(), nn.Linear(48, 48), nn.Tanh(), nn.Linear(48, 25))+    net = nn.Sequential(nn.Linear(26, 48), nn.Tanh(),+                        nn.Linear(48, 48), nn.Tanh(), nn.Linear(48, 25))     loss_fn = SamplesLoss("sinkhorn", blur=0.05)      def f(z, t):@@ -164,7 +210,7 @@ def main():         loss = l_sink + lam_v * vel         if lam_m > 0:             pen = 0.0-            for j in (1, 2, 3):  # intermediate steps 2,3,4 (0-indexed 1..3)+            for j in (1, 2, 3):                 zj = steps[j][0]                 dk = torch.cdist(zj, Z).topk(k, dim=1, largest=False).values[:, -1]                 pen = pen + torch.relu(dk - r).pow(2).mean()@@ -178,31 +224,124 @@ def main():         if epoch - best_epoch >= 50 or time.time() - t_start > 120:             break -    # ---- extrapolate selected last-stage cells -----    sel = rng.choice(last.n_obs, size=n_out, replace=(last.n_obs < n_out))-    if last.n_obs >= n_out:-        sel.sort()-    Xsel = last.X[sel].astype(np.float32)-    Xh = Xsel.toarray()[:, hvg]-    z_last = torch.from_numpy(encode(Xh))+    # ---- velocity at t=1 for all last-stage cells ----+    Xall = last.X.astype(np.float32)+    Xall_h = Xall.toarray()[:, hvg]+    z_all = torch.from_numpy(encode(Xall_h))     with torch.no_grad():-        z = z_last-        for _ in range(n_units):-            z = integrate(z)-        z_pred = z_last + alpha * (z - z_last)-        Xh_pred = decode(z_pred.numpy())-        resid = Xh - decode(encode(Xh))-        Xh_out = np.clip(Xh_pred + resid, 0.0, None)--    # ---- assemble full-panel output: non-HVG genes copied from input cells -----    coo = Xsel.tocoo()+        v_all = f(z_all, torch.tensor(1.0)).numpy()+    del Xall_h++    vnorm = np.linalg.norm(v_all, axis=1)++    # ---- between-type composition: frequency-trend extrapolation ----+    lab0 = prev.obs["celltype"].astype(str).to_numpy()+    lab1 = last.obs["celltype"].astype(str).to_numpy()+    T = min(T_raw, 2.0) * float(os.environ.get("TSCALE", "1"))+    types = np.unique(lab1).tolist()+    c0 = {t: float((lab0 == t).sum()) for t in np.unique(lab0).tolist()}+    wtype = []+    s = 5.0+    for t in types:+        c1t = float((lab1 == t).sum())+        ratio = (c1t + s) / (c0.get(t, 0.0) + s)+        wtype.append(c1t * float(np.clip(ratio, 0.2, 5.0)) ** T)+    wtype = np.asarray(wtype, dtype=np.float64)+    from src.task1_temporal.reweight import largest_remainder+    alloc = largest_remainder(wtype, n_out)++    # ---- within-type selection by velocity norm (mechanism), uniform if off ----+    sel_parts = []+    for t, n in zip(types, alloc):+        if n <= 0:+            continue+        pool = np.flatnonzero(lab1 == t)+        n = int(n)+        if mech_off:+            ch = rng.choice(pool, size=n, replace=pool.size < n)+        else:+            med = max(np.median(vnorm[pool]), 1e-8)+            ratio = np.clip(vnorm[pool] / med, 0.2, 3.0)+            w = np.maximum(1.0 + gamma * (ratio - 1.0), 1e-3)+            ch = pool[weighted_sample(len(pool), n, w, rng)]+        sel_parts.append(ch)+    sel = np.sort(np.concatenate(sel_parts))+    Xsel = Xall[sel]+    Xh = Xsel.toarray()[:, hvg]++    if alpha > 0:+        v_sel = v_all[sel]+        vm = v_sel.mean(axis=0)+        Vc = v_sel - vm+        # top-K right singular directions of the velocity cloud+        _, _, Vt = np.linalg.svd(Vc, full_matrices=False)+        Vk = Vt[:K]+        v_proj = vm + (Vc @ Vk.T) @ Vk+        delta = (v_proj @ comp).astype(np.float32)  # (n_out, n_hvg)+        gstd = Xh.std(axis=0)+        shift = alpha * delta+        mask = np.abs(shift) > (0.01 * gstd)[None, :]+        Xh = np.clip(Xh + shift * mask, 0.0, None)+        stats = dict(+            frac_cells_modified=float((np.abs(shift * mask).max(axis=1) > 0).mean()),+            frac_genes_modified=float((np.abs(shift).max(axis=0) > 0.01 * gstd).mean()),+            shift_std=float(shift[mask].std()) if mask.any() else 0.0,+            w_min=float(w.min()), w_max=float(w.max()),+        )+        print(json.dumps(stats), flush=True)+     hvg_set = np.zeros(last.n_vars, dtype=bool)     hvg_set[hvg] = True+    coo = Xsel.tocoo()     keep = ~hvg_set[coo.col]     base = sp.coo_matrix((coo.data[keep], (coo.row[keep], coo.col[keep])), shape=coo.shape)-    nz = Xh_out.nonzero()-    blk = sp.coo_matrix((Xh_out[nz], (nz[0], hvg[nz[1]])), shape=coo.shape)-    write_out(args.out, (base + blk).tocsr(), genes)+    nz = Xh.nonzero()+    blk = sp.coo_matrix((Xh[nz].astype(np.float32), (nz[0], hvg[nz[1]])), shape=coo.shape)+    return (base + blk).tocsr()+++def main():+    p = argparse.ArgumentParser()+    p.add_argument("--data", required=True)+    p.add_argument("--out", required=True)+    p.add_argument("--seed", type=int, required=True)+    args = p.parse_args()++    seed = args.seed+    rng = np.random.default_rng(seed)+    torch.manual_seed(seed)++    mech_off = os.environ.get("MECH_OFF", "0") == "1"+    ode_off = os.environ.get("ODE_OFF", "0") == "1"++    view = args.data+    genes = load_genes(view)+    with open(os.path.join(view, "manifest.json")) as f:+        man = json.load(f)+    inputs = sorted(man["inputs"], key=lambda e: e["time"])+    min_c, max_c = man["min_cells"], man["max_cells"]+    t_target = man["target"]["time"]++    last = read_input(view, inputs[-1])+    n_out = int(np.clip(last.n_obs, min_c, max_c))++    alpha = float(os.environ.get("ALPHA", "0.0"))+    K = int(os.environ.get("K", "5"))+    gamma = float(os.environ.get("GAMMA", "0.5"))+    lam = float(os.environ.get("LAM", "0.15"))++    if ode_off:+        X = uniform_copy(last, n_out, rng)+    elif len(inputs) < 2:+        X = single_input_path(last, n_out, genes, rng, mech_off, lam)+    else:+        prev = read_input(view, inputs[-2])+        t_prev, t_last = inputs[-2]["time"], inputs[-1]["time"]+        T_raw = (t_target - t_last) / max(t_last - t_prev, 1e-6)+        X = two_input_path(prev, last, n_out, seed, rng, mech_off, alpha, K,+                           gamma, T_raw)++    write_out(args.out, X, genes)   if __name__ == "__main__":

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

完成。节点 13 提交内容：

**做了什么**：修复父节点（manifold_ode, 47.86）两个结构问题——(1) X3/两输入路径：放弃 PCA 解码位移与全局速度重加权（查分证明两者都押反方向），改为两阶段类型频率趋势外推重加权 + ODE 速度范数只做**型内**无放回加权选择（γ=0.5，α=0）；(2) proxy10 单输入路径：从裸 copy_last 改为心脏解剖组成先验重加权 + 型内增殖程序 λ=0.15（继承节点 3/7 已验证策略）。

**查分结果**（A 半，seed 0，用 8/20 次）：X3 = 52.37（父 45.41，copy_last 47.92，de_score 首次转正 +0.10）；proxy10 = 59.76（父 52.75）。估算节点分 ≈ 54.8。MECH_OFF 对照 X3 = 51.32，机制净贡献 +1.05。两视图 vec-check 通过，seed 确定。

**下一步最值得试**：proxy10 的 λ 程序产生稠密输出、拖累 variogram（限制在 HVG/稀疏保留可望回到 63+）；X3 趋势外推的 T 截断与平滑系数细扫；型内速度选择改用型内秩。

调研员

f high-velocity cells are a rare population → check mmd_u component; if worse, set γ=0. 3) proxy10 λ-program might not help if proliferation markers are uninformative → compare against node 3's proxy10 score (63.66) as target. 4) Training time: ODE training ≤120s + DPT ≤30s should fit in 30-min budget.",
  "family_id": "manifold_ode",
  "mechanism": "ODE velocity field on kNN-constrained manifold determines per-cell progression direction; only top-5 PCA-projected component of velocity is back-projected as a sparse gene-space shift (rank-5, HVG-only), while velocity norm reweights cell sampling. Single-stage fallback uses diffusion pseudotime as the velocity proxy.",
  "vs_constant_shift": "The shift is per-cell (depends on each cell's position on the manifold and its local ODE velocity), not per-type or global. The rank-5 projection means different cells get different direction/magnitude shifts based on their learned dynamics. The sampling reweighting changes composition continuously by velocity norm, not by discrete type labels.",
  "mechanism_evidence": "1) Compare variogram: rank-5 shift should preserve it (target: X3 variogram ≤ 0.0016 vs node 11's 0.0023). 2) Report per-cell shift norms: std(α·δ) should be >0 and heterogeneous (not constant). 3) Report fraction of genes/cells actually modified (>10% cells, >5% genes with |δ|>threshold). 4) de_score should turn positive on X3 (target >0 vs node 11's -0.22). 5) proxy10 de_score should exceed 0.1 (vs node 11's 0.016).",
  "mechanism_off_control": "Set MECH_OFF=1: (a) for two-input path, skip ODE training entirely, use uniform sampling (no velocity reweighting) and no shift (α=0) → output = uniform copy of last stage; (b) for single-input path, skip DPT and λ-program → output = uniform copy. If MECH_OFF output differs from mechanism-ON output by <1e-6 in element-wise L2, mechanism is not running. Expected difference: mechanism-ON should shift ≥10% of cells' expression and alter sampling weights.",
  "sources": []
}