总览 · ← 返回运行 20261003-093415-search-t1-r2-D-s0

节点 n4 在终选来历上

composition_trend + 型内拟时序表达平移（已实现并筛查；λ>0 均降 X3 加权分，默认 λ=0 出厂）

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 26100abfc5: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 6bb7e1fdf1。改动的文件：solution/METHOD.md +80 −67、solution/run.py +114 −1

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 3bc5139..335ce34 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,72 +1,85 @@-# composition_trend — type-level composition reweighting + within-type maturity selection--**Provenance.** Derived from agent-produced node 33 of run `20261002-034201-search-t1-abc-r1-A-era`-(programs.git `refs/nodes/33`, commit `10400ee`; the program behind the official T1:val 50.1, submissions.tsv-2026-10-02). The mechanism and every constant are the node's own defaults; nothing was re-tuned. What was removed,-for the seed contract only (2026-10-03):--- the external-maturity axis `A_EXT` (read manifest `source == "external"`; a view-identity read; it never fired on-  final / proxy / patched X3);-- the multi-stage pooling `POOL`, the composition-trajectory axis `A_TR`, the kNN expression smoothing `A_S`, the-  apoptosis / cell-level proliferation terms (all 0 in the node's submitted defaults) and the CellRank fate mode;-- the `inputs_by_time(include_external=False)` base selection: the base is simply the latest input stage by time;-- the `VEC_*` environment overrides (constants are fixed in the code).--On every current neutral view (final, proxy, X1, X3–X6) the seed and node 33 select the same cells; see the admission-record for the digest check. The mechanism-split study (`notes/reports/dev/2026-10-02_mech_split_50.md`, candidate-`r1A:CUS` = `r1A:CU`) is the analysis of exactly this procedure.--**Naming.** "trend" is descriptive, not a two-stage model: the type weights come from a single snapshot (types whose-cells are, on average, less proliferative get a larger share). On the official final view this moves the predicted-pseudobulk along the previous step's direction (cosine with E9.5 − E8.5: 0.33 for the composition part, 0.34 for the-whole procedure; mech_split §4), and in the E8.5 → E9.5 rehearsal the composition part carries most of the gain-(+10.0 of +11.4 points; mech_split §2.1). Nothing in the code reads a second stage.--Contract: `python run.py --data <view> --out <pred.h5ad> --seed <int>`; CPU only (`EXECUTION.json {"gpu": false}`).--## Method--Base = the latest input stage of the view (final: E9.5; proxy: E8.5; rulers: their last input), all cells, its own-`celltype` labels (whatever vocabulary the view provides; `Unknown` is an ordinary type).--1. **Cell scores** (generic gene-set means, z-scored over the stage, clipped at ±3): proliferation (34 cell-cycle-   genes), metabolic maturity `z_met` = OXPHOS (13 genes) − glycolysis (10 genes).-2. **Within-type commitment** `z_fate`: HVG 2000 → PCA 30 → kNN 30 → diffusion map 15 → diffusion pseudotime rooted-   at the most progenitor-like cell (argmax 2·z_prolif − z_met, first index on ties); z-scored, the type mean-   removed, z-scored again (only the order inside a type matters).-3. **Weights.** Type layer `w_t = exp(−0.55 · z(type-mean z_prolif))` (types with < 5 cells: 1). Cell layer-   `w_i = exp(1.2 · z_met_i + 0.6 · z_fate_i)`. `w = clip(w_t · w_i, 1e-6, 1e6)`.-4. **Sampling.** n = the stage's cell count clipped to [min_cells, max_cells] (final: 5,118 of 17,057). Per-type-   quota ∝ the type's weight sum (largest remainder, capped by type size, overflow re-apportioned); inside a type 5-   equal-frequency `z_met` strata, stratum quotas ∝ stratum weight sums, Efraimidis–Spirakis weighted sampling-   without replacement inside a stratum. Expression values are copied unchanged.--Mechanism-off controls (for analysis; not code switches): `A_TP = 0` leaves the input composition (mech_split "U");-`A_CM = A_FATE = 0` with the same per-type quotas is uniform within type (mech_split "C").--## Data / knowledge used--Only the latest input stage of the view. Generic knowledge: textbook cell-cycle, OXPHOS and glycolysis gene sets-(pathway membership, not stage measurements). No held-out stage, no information from (E9.5, E13.5], no external-dataset, no `uns.celltype_palette`, no frozen probe, no pre-trained weights.+# composition_trend + 型内拟时序表达平移（已实现并筛查；λ>0 均降 X3 加权分，默认 λ=0 出厂）++Parent (node 3, composition_trend): type-level composition reweighting + within-type maturity+selection of real cells; expression never modified. This node adds, per PLAN+(family `composition_program`), a gene-specific, type-specific expression shift along+within-type pseudotime trends with global shrinkage — implemented, verified working, and+screened on both rulers. **The mechanism failed the PLAN's own success criterion and ships+disabled (LAM=0 default, `VEC_LAM` env re-enables); with λ=0 the program is byte-identical to+the parent on both views.**++## Method (delta vs parent)++Cell selection, weights, sampling and n are unchanged. After selecting the output cells, when λ≠0:++1. **Within-type slopes.** For each cell type with ≥10 cells in the latest input stage, OLS+   slope β[t,g] of gene g against the parent's z_fate (within-type centred diffusion+   pseudotime). Genes kept only if detected in ≥2% of the type's cells and standardized slope+   |r| = |β·σ_g/σ_z| ≥ 0.1; others β=0. Types with <10 cells fall back to the global all-cell+   slope (same filters).+2. **Shift.** X[i,g] += λ·Δ·β[t,g] where X[i,g]>0 (ACT_TOPQ=0), clipped at ≥0. Δ=0.3.+   Zero-activation of top-5% positive-slope genes was implemented (ACT_TOPQ) and tested.+3. **Variance guard.** Genes whose output std collapses below 0.5× unshifted std are reverted.+4. **Single-stage.** Slopes use only the latest input stage; identical code path on single- and+   multi-input views. No absolute times, no view identity, no file names read.++## Mechanism-off control and evidence the mechanism runs++- `VEC_LAM=0` output is **byte-identical to parent node 3** on both views (verified: X3 and+  proxy matrix diffs = 0 nonzero).+- λ=0.15/ACT_TOPQ=0.05: 0.82M (X3) / 9.1M (proxy) matrix entries changed; ~0.5% of genes move+  pseudobulk by >0.01 (below PLAN's hoped 20% — the detection+|r| filters are restrictive).+- λ=0.3/ACT_TOPQ=0: 0.46M / 6.8M entries changed; pseudobulk cosine with parent 0.99998.+- DE metrics are scale-invariant, so λ only matters through the *mix* of shift vs composition+  in dp; de_score was identical at λ=0.3 and λ=1.0 (−0.2143), i.e. the shift dominates the+  dp ranking from λ≈0.3 up and moves it the *wrong way* on X3.++## Screen results (vec-score A-half, seed 0; parent: X3 48.71 / proxy10 63.66 → node 53.69)++| config | X3 pts (de_score / de_dir / mmd / vario) | proxy10 pts | node score (X3×2+proxy)/3 |+|---|---|---|---|+| λ=0 (= parent) | 48.71 (−0.182 / 0.026 / .472 / .533 skill) | 63.66 | 53.69 |+| λ=0.15, ACT_TOPQ=0.05 | (de_dir skill .505, mmd .468, vario **.478**) | ~63.6 | < parent |+| λ=0.3, ACT_TOPQ=0 | 48.20 (−0.214 / 0.011 / .470 / .527) | 63.95 | 53.45 |+| λ=1.0, ACT_TOPQ=0 | 47.93 (−0.214 / 0.008 / .470 / .516) | — | < 53.45 |++Findings: (a) zero-activation (ACT_TOPQ>0) clearly hurts variogram → shipped 0; (b) on X3+(weight ×2) the forward pseudotime shift monotonically degrades de_score, de_direction, mmd_u+and variogram — the within-type maturity axis re-amplifies exactly the signal composition+selection already pushes, and X3 truth does not reward more of it; (c) proxy10 gains +0.3 at+λ=0.3 (mmd_u 0.651→0.662), within noise and outweighed 2:1 by X3. PLAN required ≥3-point X3+de_score gain for success; measured direction is negative → mechanism falsified, ships OFF.++## Verified / not verified++- Verified: λ=0 ≡ parent on proxy and X3; final default code runs on both views (proxy 82 s,+  X3 ~60 s, seed 0) and passes vec-check; determinism (no RNG in the shift path).+- Not verified: λ<0 (shift toward progenitors — biologically backwards, not screened);+  Δ independent of λ (only the product enters); seeds >0; the final two-input view (code path+  identical by construction).+- Queries used: 5 of 20.  ## Hyper-parameters  | Name | Value | Where it came from | |---|---|---|-| `A_TP` | −0.55 | node 33's default (tuned by the agent on the old proxy / proxy2) |-| `A_CM` | 1.2 | node 33's default (same) |-| `A_FATE` | 0.6 | node 33's default (agent scan +0.2 … +1.0 on proxy seed 0, peak at 0.6) |-| `K_STRAT`, `MIN_TYPE_CELLS` | 5, 5 | node 33's defaults |-| HVG / PCA / kNN / diffmap | 2000 / 30 / 30 / 15 | node 33's defaults |--All were chosen on the old single-input proxy (E8.5 → E9.5), which is also where they look best; on the two-input-rulers the mechanism is near neutral (admission record).--## Known failure modes--- The type layer is a single-snapshot heuristic: it bets that low-proliferation types expand next. Where the next-  step's composition change does not go that way, every ranked metric gets worse (mech_split zero-development control).-- Within-type selection pushes the sample towards OXPHOS-high / late-pseudotime cells; on patched X3 this part was-  −2.4 points (MMD −1.8) on top of the composition (mech_split §3).-- Real cells only: no new expression states, no new cell types.+| LAM (λ) | 0.0 | screen above (0.15/0.3/1.0 all net-negative on the weighted score) |+| DELTA (Δ) | 0.3 | PLAN default (only λ·Δ matters) |+| DET_MIN / r_min / MIN_SLOPE_CELLS | 0.02 / 0.1 / 10 | PLAN + risk mitigation 1 |+| ACT_TOPQ | 0.0 | PLAN 0.05 tested; degraded X3 variogram skill 0.533→0.478 |+| parent constants | unchanged | node 3 |++Knowledge used: same as parent (textbook cell-cycle / OXPHOS / glycolysis gene sets); the shift+uses no external knowledge — slopes are estimated from the view's own data. No held-out stage,+no forbidden-window information, no external dataset.++## Suggested next direction++The maturity/pseudotime axis is exhausted on X3 (both composition and expression pushes along+it degrade de_score below chance). A different signal is needed for X3's de_recovery — e.g.+slope estimation against a trajectory axis rooted differently (lineage-branching direction from+the prior/TF-network resources), or per-gene shifts constrained by CollecTRI regulator activity+rather than pseudotime. Also worth testing: λ<0 was never screened and X3's negative de_score+means the parent's dp is anti-correlated with truth — a *dampening* (not reversal) of the+maturity component of dp might lift X3 de_score toward 0, but must be checked against proxy10+where the same component is beneficial.diff --git a/solution/run.py b/solution/run.pyindex b7ef52b..2027299 100644--- a/solution/run.py+++ b/solution/run.py@@ -38,6 +38,15 @@ A_FATE = 0.6      # cell layer: within-type diffusion pseudotime (more committed K_STRAT = 5       # z_met strata per type MIN_TYPE_CELLS = 5 +LAM = 0.0         # global shrinkage of the within-type pseudotime expression shift.+                  # Screened 0.15/0.3/1.0 (ACT_TOPQ 0/0.05, seed 0): every lam>0 lowered the+                  # weighted node score (X3 de_score -0.18 -> -0.21, de_direction 0.026 -> 0.011,+                  # monotone in lam; proxy10 +0.3 at best). Ships OFF; set VEC_LAM to re-enable.+DELTA = 0.3       # pseudotime advance (in z_fate units) for the shift+MIN_SLOPE_CELLS = 10   # types below this fall back to the global (all-cell) slope+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)+ CYCLE = [     "Mki67", "Top2a", "Pcna", "Ccna2", "Ccnb1", "Ccnb2", "Ccnd1", "Ccneg", "Ccne1",     "Cdk1", "Cdk2", "Cdk4", "Cdk6", "Mcm2", "Mcm3", "Mcm4", "Mcm5", "Mcm6", "Mcm7",@@ -154,6 +163,101 @@ def stratified_sample(w, z_strat, inv, n_types, n_out, K, rng):     return np.sort(np.concatenate(out)) if out else np.empty(0, dtype=np.int64)  +def type_slopes(X, z_fate, inv, n_types, det_min=DET_MIN, r_min=0.1):+    """OLS slope of each gene vs within-type pseudotime, per cell type.++    beta[t, g] = cov(x_g, z_fate | t) / var(z_fate | t); genes that are too sparse+    (<det_min detected) or whose standardized slope is |r| < r_min get beta = 0.+    Types with < MIN_SLOPE_CELLS cells fall back to the global (all-cell) slope.+    """+    from scipy import sparse as sp++    n_cells, n_genes = X.shape+    Xc = X.tocsr()+    beta = np.zeros((n_types, n_genes), dtype=np.float64)++    def slope_for(rows):+        z = z_fate[rows]+        zc = z - z.mean()+        zz = float(zc @ zc)+        if zz < 1e-9 or len(rows) < 2:+            return None, None+        Xs = Xc[rows]+        b = np.asarray(zc @ Xs).ravel() / zz          # cov/var (zc sums to 0)+        mu = np.asarray(Xs.mean(axis=0)).ravel()+        sq = np.asarray(Xs.multiply(Xs).mean(axis=0)).ravel()+        var_g = np.maximum(sq - mu * mu, 0.0)+        det = np.asarray(Xs.getnnz(axis=0)).ravel() / len(rows)+        return b, (var_g, det)++    g_beta, g_stats = slope_for(np.arange(n_cells))+    if g_beta is None:+        return beta+    g_std_z = float(np.std(z_fate))+    g_r = g_beta * np.sqrt(g_stats[0]) / max(g_std_z, 1e-9)+    g_keep = (g_stats[1] >= det_min) & (np.abs(g_r) >= r_min)+    g_beta = np.where(g_keep, g_beta, 0.0)++    for t in range(n_types):+        rows = np.where(inv == t)[0]+        if len(rows) < MIN_SLOPE_CELLS:+            beta[t] = g_beta+            continue+        b, stats = slope_for(rows)+        if b is None:+            beta[t] = g_beta+            continue+        std_z = float(np.std(z_fate[rows]))+        r = b * np.sqrt(stats[0]) / max(std_z, 1e-9)+        keep = (stats[1] >= det_min) & (np.abs(r) >= r_min)+        beta[t] = np.where(keep, b, 0.0)+    return beta+++def apply_shift(Xsel, inv_out, beta, lam, delta, act_topq=ACT_TOPQ, chunk=1024):+    """X + lam*delta*beta[type] where X>0 or beta is in the top act_topq positive+    slopes of the cell's type; clipped at >=0. Per-gene variance guard: genes whose+    output std collapsed below 0.5x are reverted. lam=0 leaves X untouched."""+    from scipy import sparse as sp++    Xc = sp.csr_matrix(Xsel)+    n_out, n_genes = Xc.shape+    orig_std = np.asarray(Xc.multiply(Xc).mean(axis=0)).ravel() - np.asarray(Xc.mean(axis=0)).ravel() ** 2+    orig_std = np.sqrt(np.maximum(orig_std, 0.0))++    scale = float(lam) * float(delta)+    # per-type activation mask for zero entries (top positive slopes)+    n_act = int(round(act_topq * n_genes))+    act_mask = np.zeros((beta.shape[0], n_genes), dtype=bool)+    if scale != 0.0 and n_act > 0:+        for t in range(beta.shape[0]):+            b = beta[t]+            pos = np.where(b > 0)[0]+            if len(pos) == 0:+                continue+            if len(pos) > n_act:+                pos = pos[np.argsort(-b[pos], kind="stable")[:n_act]]+            act_mask[t, pos] = True++    cols = np.arange(n_genes)+    out = np.empty((n_out, n_genes), dtype=np.float32)+    for s in range(0, n_out, chunk):+        e = min(s + chunk, n_out)+        D = np.asarray(Xc[s:e].todense(), dtype=np.float32)+        types = inv_out[s:e]+        for j, t in enumerate(types):+            b = (scale * beta[t]).astype(np.float32)+            m = (D[j] > 0) | act_mask[t]+            D[j] = np.where(m, np.maximum(D[j] + b, 0.0), D[j])+        out[s:e] = D++    new_std = np.sqrt(np.maximum((out ** 2).mean(axis=0) - out.mean(axis=0) ** 2, 0.0))+    bad = (orig_std > 1e-6) & (new_std < 0.5 * orig_std)+    if bad.any():+        out[:, bad] = np.asarray(Xc[:, bad].todense(), dtype=np.float32)+    return sp.csr_matrix(out)++ def main() -> None:     ap = argparse.ArgumentParser()     ap.add_argument("--data", required=True)@@ -188,7 +292,16 @@ def main() -> None:     n_out = target_n_cells(manifest, adata.n_obs)     rng = np.random.default_rng(args.seed)     idx = stratified_sample(w, z_met, inv, len(uniq), n_out, K_STRAT, rng)-    write_prediction(X[idx], genes, args.out, seed=args.seed)++    import os+    lam = float(os.environ.get("VEC_LAM", LAM))       # mechanism-off control: VEC_LAM=0+    delta = float(os.environ.get("VEC_DELTA", DELTA))+    actq = float(os.environ.get("VEC_ACTQ", ACT_TOPQ))+    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)+    write_prediction(Xsel, genes, args.out, seed=args.seed)   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。做了什么、查分结果、下一步：

**做了什么**：按 PLAN 在父节点（node 3, composition_trend）上实现了型内拟时序回归表达平移（OLS 斜率 β×λ·Δ，检出率/标准化斜率过滤，全局斜率回退，方差守卫，零激活开关）。机制对照验证：λ=0 与父节点在两视图逐字节相同；λ=0.15/0.3 确实改变了 0.5M–9M 个矩阵元素。

**查分结果**（5/20 次额度，seed 0）：λ=0.3+ACTQ=0 → X3 48.20（父 48.71）、proxy10 63.95（父 63.66），加权节点分 53.45 < 53.69；λ=1.0 更差；零激活（ACT_TOPQ=0.05）明显伤 X3 variogram（skill 0.533→0.478）。X3 上 de_score/de_direction 随 λ 单调恶化——拟时序平移放大的正是组成选择已在推的成熟度轴，X3 真值不奖励更多。**机制按 PLAN 自身标准（X3 de_score ≥+3 分）被证伪，出厂默认 λ=0**（=父节点行为，82s 跑完 proxy，两视图均过 vec-check），METHOD.md 完整记录证据与数据。

**下一步最值得试**：X3 的 de_score 为负（低于 chance）说明父节点 dp 与真值反相关，成熟度轴已耗尽；应换信号源——用 prior/（CollecTRI 调控网络）约束的基因方向平移，或对成熟度分量做衰减（λ<0 方向未筛查，但需同时盯 proxy10，那里该分量是有益的）。

调研员

get.",
  "family_id": "composition_program",
  "mechanism": "Within each cell type, estimate gene-specific expression trends along diffusion pseudotime (already computed by parent). Extrapolate a small step along these trends for selected cells, with global shrinkage λ. This adds a gene-directional signal to the pseudobulk that composition reweighting alone cannot provide.",
  "vs_constant_shift": "A constant per-type shift applies the same vector to all cells of a type regardless of gene identity. Here the shift is gene-specific (each gene has its own slope from pseudotime regression), cell-position-dependent (magnitude can scale with the cell's pseudotime position), and type-specific (slopes are estimated within each type separately). The direction comes from data-driven within-type trajectories, not from a hand-written displacement vector. It also preserves sparsity structure by only activating top-slope genes at zeros.",
  "mechanism_evidence": "Engineer should verify: (1) With λ>0, the per-gene dp (pseudobulk change vs reference) has non-zero correlation with the within-type slopes (confirming the shift is actually changing gene rankings). (2) de_score and de_direction on X3 improve by ≥0.05 raw units. (3) The fraction of genes with |shift| > 0.01 is substantial (>20% of genes), confirming the mechanism touches many genes. (4) Four-group breakdown: de_recovery should rise, mmd_u should not drop >2 points. (5) Compare output pseudobulk cosine with parent pseudobulk: should be <0.99 (expression actually changed, not just composition).",
  "mechanism_off_control": "Set λ=0 in the same program. This zeroes out the expression shift entirely; the output should be identical to parent node 3 (same cells selected, expression unmodified). Engineer runs once with λ=0 and once with λ=0.15, confirms (a) λ=0 score matches parent ≈53.69, (b) λ=0.15 changes de_score/de_direction. If outputs are identical for λ=0 and λ=0.15, the mechanism is not running.",
  "sources": []
}
```