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节点 n17

在父节点基础上，把两阶段型内位移的方向来源从原始伪批量 Δμ 换成 CollecTRI TF→靶基因网络传播方向（typed，β_net=4，EB 收缩+稀疏门控；仅双输入视图激活，单输入与父逐字节一致）

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 dc1acabb7b: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 ba44b165dc。改动的文件：solution/METHOD.md +79 −72、solution/run.py +204 −16

diff --git a/solution/METHOD.md b/solution/METHOD.mdindex 2a3e363..f17a98b 100644--- a/solution/METHOD.md+++ b/solution/METHOD.md@@ -1,78 +1,85 @@-# composition_trend + α=3 重锚定 + 两阶段逐基因方向位移（按细胞型 EB 收缩，β=1；仅双输入视图激活，X3 A 半 54.51→54.88，proxy10 与父逐字节一致）--Parent (node 10, 55.86): composition_trend weighted sampling + per-type re-anchoring α=3.0.-PLAN asked for a two-stage gene-level EB-shrunk direction shift to lift X3 de_recovery-(de_score −0.052, below floor). Implemented as specified, plus a per-type variant found by screening.--## Method (family: composition_program; mechanism = two-stage pseudobulk delta direction shift)--Active only when the view has ≥2 input stages (sorted by time; uses the last two):--1. Δμ_g = pb(later) − pb(earlier), log space. Two modes:-   - `global`: one Δμ per gene; EB shrinkage w_g = n_g/(n_g+K), K=30, n_g = detected-cell count-     over both stages;-   - `type` (ships): per-cell-type Δμ_t using the type's cells in each stage, shrunk by-     min(n1_g,n2_g)/(min+K); genes with <20 detections in either stage of that type fall back to-     the global shrunk Δμ.-2. Sparse gate: keep |Δμ_s| > ZMIN·median(|Δμ_s|), ZMIN=1.5.-3. Apply to each sampled cell after α=3 recentering: x[i,g] += clip(β·Δμ_s, ±MAX_SHIFT), MAX_SHIFT=1.0,-   only at existing nonzeros, clipped ≥0 (sparsity and covariation preserved). β=1.0 ships.-4. Single-input views (proxy10): block never runs → output byte-identical to parent node 10-   (verified via h5 X/data comparison). No RNG in the block; deterministic.--Env knobs: VEC_SHIFT_BETA / VEC_SHIFT_K / VEC_SHIFT_ZMIN / VEC_SHIFT_MAX / VEC_SHIFT_MODE.--## Screen (vec-score A-half, seed 0; X3 parent = 54.51)+# 在父节点基础上，把两阶段型内位移的方向来源从原始伪批量 Δμ 换成 CollecTRI TF→靶基因网络传播方向（typed，β_net=4，EB 收缩+稀疏门控；仅双输入视图激活，单输入与父逐字节一致）++Parent (node 14, 56.39): composition_trend weighted sampling + per-type re-anchoring α=3.0 ++two-stage typed pseudobulk-Δμ direction shift (β=1). PLAN target: lift de_recovery (X3 de_score+−0.026, below floor) by replacing the noisy raw Δμ with a CollecTRI TF→target network-propagated+direction. Family: composition_program; mechanism = network-constrained gene-level direction shift.++## Method change vs parent++Active only when the view has ≥2 input stages (uses the last two, sorted by time). The shift+direction source changes from raw per-type pseudobulk Δμ to:++1. **Network load** (`load_tf_network`): `<view>/prior/tf_regulons/*.tsv.gz` (sorted glob, relative+   layout only — view-agnostic). CollecTRI mouse: 37,497 signed TF→target edges; keeps+   `sign_known==1`, `mor∈{+1,−1}`, both genes in the panel. On X3: 357 TFs kept (≥10 panel targets,+   TF detected in ≥10 cells in both stages), 25,013 edges, 5,208 regulated genes. Missing file or+   <10 kept TFs → automatic fallback to the parent typed-Δμ path (PLAN off-control guarantee).+2. **TF activity change**: Δact[type, tf] = mean of the per-type pseudobulk delta (low-detection+   genes fall back to the global delta, as in the parent) over the tf's panel targets. Aggregating+   ~70 targets/TF is what stabilizes the estimate at X3's ~65 cells/type — the point of the PLAN.+3. **Propagation**: dir[type, g] = Σ_{tf→g} sign(tf→g)·Δact[type, tf]; genes without regulators+   get dir=0 (network sparsity is the gate's skeleton).+4. **EB shrink + rescale + sparse gate**: w_g = n_reg(g)/(n_reg(g)+K_net), K_net=5; dir rescaled so+   median|dir| over regulated genes equals the parent's median|Δμ_shrunk| (β_net comparable to the+   parent's β units); gate |dir| > ZMIN_net·median(|dir| nonzero), ZMIN_net=1.5.+5. **Apply**: x[i,g] += clip(β_net·dir_g, ±MAX_SHIFT=1.0) at existing nonzeros only, clip ≥0+   (sparsity/covariation preserved, same writer as the parent). No RNG in the block; deterministic.++Env knobs: VEC_NET_BETA (0 = off control) / VEC_NET_K / VEC_NET_ZMIN / VEC_NET_MODE / VEC_NET_STACK.++## Mechanism-off control (verified)++- VEC_NET_BETA=0 on X3: X output **byte-identical** to parent node-14 code run (h5 data/indices/+  indptr compared, twice, incl. after the final restructure).+- Single-input view (proxy, E8.5 only): whole block inactive; stderr shows n_input_stages=1 and no+  gene_shift; output path identical to parent's → proxy10 score unchanged (58.80 parent B-half).+  vec-check ok on both views; X3 final run byte-identical to the scored β=4 prediction.++## Screen (vec-score A-half, X3, seed 0; parent node14 = 54.88, node10 = 54.51)  | variant | X3 board | de_score | de_dir | mmd_u | variogram | |---|---|---|---|---|---|-| global β=0.5 | 54.27 | −0.014 | 0.076 | 0.0278 | 0.001215 |-| global β=5 | 47.46 | −0.229 | −0.008 | 0.0360 | 0.001601 |-| global β=20 | 38.53 | −0.157 | −0.061 | 0.0581 | 0.003925 |-| type β=5 | 53.71 | −0.057 | 0.022 | 0.0250 | 0.001412 |-| type β=15 | 48.35 | −0.129 | −0.028 | 0.0296 | 0.002165 |-| **type β=1 (ships)** | **54.88** | −0.043 | 0.075 | 0.0259 | 0.001206 |-| type β=2 | 54.88 | −0.057 | 0.057 | 0.0250 | 0.001232 |-| type β=3 | 54.88 | −0.029 | 0.043 | 0.0246 | 0.001279 |--- **Global mode is actively harmful when amplified** (PLAN risk #3 realized): the whole-embryo-  E8.75→E9.0 Qiu delta, scaled up, moves X3 in the wrong direction on all four metrics. Ships OFF.-- **Typed mode gives a small, flat-topped gain** (β=1/2/3 all 54.88, +0.37 over parent, within the-  ~2-pt noise): the gain is mmd_u (0.0272→0.0259, +0.6 pts), de_score/de_direction ~unchanged at-  β=1, variogram unchanged. **The PLAN's target group (de_recovery) was NOT lifted** — X3 de_score-  stays below floor. The honest read: per-type deltas mostly reposition cell states slightly toward-  the later stage (helps MMD), not a true DE direction recovery.-- β=1 chosen over 2/3 (identical board) because it preserves de_direction best (0.075 vs 0.043).--## Mechanism evidence (off control & diagnostics, X3 seed 0)--- Off control VEC_SHIFT_BETA=0: X3 output byte-identical to parent run (verified).-- Active (typed β=1): 186,400 (type,gene) pairs pass the gate (median gate over |Δμ_s|);-  1.18M nonzero entries touched, all changed; mean |shift| ≈ 0.041 log units; shifts are-  type-specific (not a constant vector): per-type Δμ differ by construction; >0 within-type-  dispersion since gate+fallback differ per type. Cells changed: all 652 sampled X3 cells.-- Proxy10 default run byte-identical to parent (1 input → block inactive), so proxy10 score is-  unchanged by construction (no query spent re-verifying).+| net typed β=1 | 54.68 | −0.014 | 0.084 | 0.0272 | 0.00120 |+| net typed β=2 | 54.94 | +0.014 | 0.083 | 0.0271 | 0.00120 |+| net typed β=3 | 55.20 | +0.043 | 0.081 | 0.0271 | 0.00119 |+| **net typed β=4 (ships)** | **55.33** | **+0.057** | 0.079 | 0.0270 | 0.00119 |+| net typed β=5 | 55.07 | +0.029 | 0.078 | 0.0270 | 0.00119 |+| net typed β=6 | 54.95 | +0.014 | 0.077 | 0.0270 | 0.00120 |+| net typed β=8 | 54.78 | 0.000 | 0.075 | 0.0270 | 0.00120 |+| net β=4, ZMIN=2.0 | 55.33 | +0.057 | 0.080 | 0.0270 | 0.00119 |+| stack (parent Δμ β=1 then net β=4) | 55.29 | 0.000 | 0.072 | 0.0257 | 0.00120 |+| (parent node14, typed Δμ β=1) | (54.88) | (−0.026) | (0.092) | (0.0251) | (0.00121) |++- **de_score flips positive for the first time in this tree** (+0.057 at β=4, skill >0.5, above+  floor) and is non-monotone in β with a clear peak at 4 — consistent with "right direction,+  wrong amplitude" (PLAN risk #3): over-amplification (β≥6) degrades it again.+- **Stacking with the raw typed Δμ kills the DE signal** (de_score back to 0) even though it keeps+  the mmd_u gain (0.0257): the noisy Δμ shift re-orders genes and drowns the network direction.+  Ships unstacked — a clean test of the PLAN mechanism, at the cost of node-14's mmd_u edge+  (0.0270 vs 0.0251, ~+0.6 pts given back; net de_recovery/direction gain outweighs it: +0.45 A-half).+- de_direction slightly lower than the parent (0.079 vs 0.092) — the network direction is sparser+  (5,208 regulated genes vs ~28k for Δμ), so unregulated genes contribute nothing to the rank+  correlation; the gated-gene ordering is what improves de_score.+- Mechanism evidence per PLAN: 357 TFs × 10 types, 26,030 (type,gene) entries pass the gate and are+  shifted (192,899 nonzero entries touched, mean |shift| 0.135 at β=4); per-type direction differs+  from the type-mean by 0.0022 on average (typed mode is genuinely per-type, not a global vector);+  gene set shifted = regulated genes above the gate, a sparse subset structurally distinct from the+  parent's dense Δμ set (Jaccard ≪1 by construction: only 5,208/28,000 genes are even eligible).++## Knowledge sources++- CollecTRI signed TF→target network: provided in `prior/tf_regulons/` of each view (general+  regulatory knowledge, not derived from any held-out stage/genotype; allowed per task rules).+- No held-out stage (E10.5/E12.5, 9.5<E≤13.5) or held-out genotype information used; the program+  reads only the view's inputs, `genes.txt`, and `prior/`. No absolute-time, path, filename, or+  manifest-identity branching (only input count and time *differences*).  ## Verified / not verified -- Verified: vec-check ok on proxy and X3; off-control byte-identity; proxy byte-identity;-  default == VEC_SHIFT_BETA=1 VEC_SHIFT_MODE=type run; determinism (no RNG in block).-- Not verified: B-half (A-half gain +0.37 is within noise — may not replicate); seeds ≠0; K/ZMIN-  grids (only β scanned); behaviour on the final view (E8.5+E9.5 official, larger types) — the-  typed delta there is official-data-based, expected better-powered than X3's 2.2k-cell Qiu stages.-- Queries used: 9 of 20.--## Knowledge used--None beyond parent (textbook cell-cycle/OXPHOS/glycolysis sets). No external files, no held-out-stage info, no forbidden-window measurements; the two-stage delta is computed live from the view's-own inputs. View-independent: uses only input order by time and time differences implicitly (none-hard-coded), no manifest identity fields, no absolute times, no file names.--## Next direction--Typed deltas help mmd_u only; de_recovery on X3 remains sub-floor for every variant tried. A-direction signal from prior/ regulatory knowledge (CollecTRI TF targets, Reactome cardiac-pathways) constrained to genes with two-stage support is the untested lever; alternatively accept-de_recovery as floor-bound on X3 and push mmd_u (composition) further.+- Verified: off control byte-identity; single-input inactivity; both views run + vec-check; β/ZMIN+  screen above (A-half only, seed 0; quantized de_score steps of 1/70 mean ±1 step is luck).+- Not verified: B-half reproduction (+0.45 A-half is below the ~2-pt noise); whether β=4 transfers+  to final (E8.5→E9.5 official, larger cells/type — the rescale-to-Δμ-median makes β units+  comparable, but the optimum could shift); no multi-seed run within this node's budget.+- proxy10 unchanged by construction (single input); not re-scored (quota preserved).diff --git a/solution/run.py b/solution/run.pyindex bcfe293..7c22922 100644--- a/solution/run.py+++ b/solution/run.py@@ -75,6 +75,29 @@ SHIFT_K = 30.0         # EB shrinkage constant (detection-count based) SHIFT_ZMIN = 1.5       # sparse gate: multiple of median |dmu_shrunk| SHIFT_MAX = 1.0        # per-gene shift cap (log space) +NET_BETA = 4.0         # CollecTRI network-propagation direction shift (replaces raw pseudobulk+                       # dmu as the direction source when >=2 input stages and the network loads).+                       # Per TF t: d_act_t = mean of the (per-type or global) pseudobulk delta over+                       # t's panel targets; per gene g: dir_g = sum_{t->g} sign(t->g)*d_act_t over+                       # kept regulators; EB shrink w_g = n_reg(g)/(n_reg(g)+NET_K); rescaled to the+                       # parent's |dmu_shrunk| median so NET_BETA is comparable to SHIFT_BETA;+                       # sparse gate |dir| > NET_ZMIN * median(|dir| over regulated genes).+                       # VEC_NET_BETA=0 -> mechanism off: falls back to the parent typed-dmu path+                       # (byte-identical to node 14). Missing/invalid network file -> same fallback.+                       # X3 A-half screen (seed 0; parent node14=54.88, node10=54.51):+                       #   beta 1/2/3/4/5/6/8 -> 54.68/54.94/55.20/55.33/55.07/54.95/54.78+                       #   de_score raw:     -0.014/+0.014/+0.043/+0.057/+0.029/+0.014/0.0+                       # Beta=4 flips de_score positive (above floor) for the first time in the tree.+                       # ZMIN 2.0 @ beta4 = 55.33 (gate near-inactive at beta=4). Stack with the+                       # parent typed dmu (dmu first, then net beta=4): 55.29 but de_score back to 0+                       # and mmd_u 0.0257 -> the raw dmu shift drowns the network DE signal; ships+                       # UNSTACKED. Single-input views (proxy10): block inactive, byte-identical+                       # to node 14 (only the n_inputs>=2 block and its helpers changed).+NET_K = 5.0            # EB shrinkage constant (regulator-count based)+NET_ZMIN = 1.5         # sparse gate: multiple of median |dir| over regulated genes+NET_MIN_TF_TARGETS = 10  # TF needs >= this many panel targets to be kept+NET_MIN_TF_CELLS = 10    # TF needs detection in >= this many cells in BOTH stages to be kept+ CYCLE = [     "Mki67", "Top2a", "Pcna", "Ccna2", "Ccnb1", "Ccnb2", "Ccnd1", "Ccneg", "Ccne1",     "Cdk1", "Cdk2", "Cdk4", "Cdk6", "Mcm2", "Mcm3", "Mcm4", "Mcm5", "Mcm6", "Mcm7",@@ -405,11 +428,154 @@ def gene_delta_by_type(view, entries, genes, labels, inv, k_shrink, min_det=20):     return out  +def load_tf_network(view):+    """CollecTRI-style signed TF->target edges from <view>/prior/tf_regulons/*.tsv.gz.++    Uses only the relative prior/ layout (identical in every view). Keeps sign_known==1 edges with+    mor in {+1,-1}. Returns [] if the directory/files/columns are missing (caller falls back).+    """+    import glob+    import gzip+    import os++    edges = []+    for path in sorted(glob.glob(os.path.join(view, "prior", "tf_regulons", "*.tsv.gz"))):+        try:+            with gzip.open(path, "rt") as f:+                hdr = f.readline().rstrip("\n").split("\t")+                if "tf" not in hdr or "target" not in hdr or "mor" not in hdr:+                    continue+                it, ig, im = hdr.index("tf"), hdr.index("target"), hdr.index("mor")+                isn = hdr.index("sign_known") if "sign_known" in hdr else None+                for line in f:+                    c = line.rstrip("\n").split("\t")+                    if len(c) <= max(it, ig, im):+                        continue+                    if isn is not None and c[isn] != "1":+                        continue+                    try:+                        m = int(float(c[im]))+                    except ValueError:+                        continue+                    if m in (1, -1):+                        edges.append((c[it], c[ig], m))+        except OSError:+            continue+    return edges+++def net_direction(view, entries, genes, labels, k_net, zmin_net, typed=True,+                  min_tf_targets=NET_MIN_TF_TARGETS, min_tf_cells=NET_MIN_TF_CELLS,+                  min_det=20):+    """CollecTRI network-propagated per-gene developmental direction (replaces raw dmu).++    d_act[type, tf] = mean of the (per-type, low-detection fallback global) pseudobulk delta over+    the tf's panel targets; dir[type, g] = sum over kept regulators sign(tf->g) * d_act[type, tf];+    EB shrink by regulator count, rescale to the parent's global |dmu_shrunk| median, sparse-gate+    by zmin_net * median(|dir|) over regulated entries. Returns (dmus_t or None, diag).+    """+    from scipy import sparse as sp++    edges = load_tf_network(view)+    gidx = {g: i for i, g in enumerate(genes)}+    n_genes = len(genes)+    uniq = np.unique(labels)+    n_types = len(uniq)++    a1 = read_stage(view, entries[-2], genes, missing="fill")+    a2 = read_stage(view, entries[-1], genes, missing="fill")+    X1, X2 = a1.X.tocsr(), a2.X.tocsr()+    lab1 = labels_of(a1) if "celltype" in a1.obs.columns else np.full(a1.n_obs, "all")+    lab2 = labels_of(a2) if "celltype" in a2.obs.columns else np.full(a2.n_obs, "all")+    gmu1 = np.asarray(X1.mean(axis=0), dtype=np.float64).ravel()+    gmu2 = np.asarray(X2.mean(axis=0), dtype=np.float64).ravel()+    gdmu_raw = gmu2 - gmu1+    gn = np.asarray(X1.getnnz(axis=0)).ravel() + np.asarray(X2.getnnz(axis=0)).ravel()+    gdmus = gdmu_raw * (gn / (gn + SHIFT_K))+    med_parent = float(np.median(np.abs(gdmus[gn > 0]))) if (gn > 0).any() else 0.0++    # per-type raw delta with low-detection fallback to the global raw delta (as in the parent path)+    D = np.tile(gdmu_raw, (n_types if typed else 1, 1))+    if typed:+        pos = {t: i for i, t in enumerate(uniq)}+        for t in np.unique(np.concatenate([lab1, lab2])):+            if t not in pos:+                continue+            r1 = np.where(lab1 == t)[0]+            r2 = np.where(lab2 == t)[0]+            if len(r1) == 0 or len(r2) == 0:+                continue+            m1 = np.asarray(X1[r1].mean(axis=0)).ravel()+            m2 = np.asarray(X2[r2].mean(axis=0)).ravel()+            n1 = np.asarray(X1[r1].getnnz(axis=0)).ravel()+            n2 = np.asarray(X2[r2].getnnz(axis=0)).ravel()+            ok = (n1 >= min_det) & (n2 >= min_det)+            D[pos[t]] = np.where(ok, m2 - m1, gdmu_raw)++    tf_nn1 = np.asarray(X1.getnnz(axis=0)).ravel()+    tf_nn2 = np.asarray(X2.getnnz(axis=0)).ravel()+    tf_targets: dict[str, list[int]] = {}+    for tf, tg, m in edges:+        if tf in gidx and tg in gidx:+            tf_targets.setdefault(tf, []).append((gidx[tg], m))+    keep_tfs = sorted(+        tf for tf, lst in tf_targets.items()+        if len(lst) >= min_tf_targets and tf in gidx+        and tf_nn1[gidx[tf]] >= min_tf_cells and tf_nn2[gidx[tf]] >= min_tf_cells+    )+    if len(keep_tfs) < 10:+        return None, {"reason": "too_few_tfs", "n_tfs": len(keep_tfs)}+    tf_pos = {tf: i for i, tf in enumerate(keep_tfs)}+    T_rows, T_cols, T_vals = [], [], []+    E_rows, E_cols, E_vals = [], [], []+    n_edges = 0+    for tf in keep_tfs:+        lst = tf_targets[tf]+        i = tf_pos[tf]+        for g, m in lst:+            T_rows.append(g); T_cols.append(i); T_vals.append(1.0 / len(lst))+            E_rows.append(i); E_cols.append(g); E_vals.append(float(m))+            n_edges += 1+    T = sp.csr_matrix((T_vals, (T_rows, T_cols)), shape=(n_genes, len(keep_tfs)))+    E = sp.csr_matrix((E_vals, (E_rows, E_cols)), shape=(len(keep_tfs), n_genes))+    n_reg = np.asarray((E.astype(bool)).sum(axis=0), dtype=np.float64).ravel()++    d_act = D @ T                       # (n_types, n_tfs): TF activity change+    dir_t = d_act @ E                   # (n_types, n_genes): propagated direction+    w = n_reg / (n_reg + float(k_net))+    dir_t = dir_t * w                   # EB shrink by regulator count+    nz = dir_t != 0+    med_dir = float(np.median(np.abs(dir_t[nz]))) if nz.any() else 0.0+    if med_dir <= 0:+        return None, {"reason": "zero_direction", "n_tfs": len(keep_tfs)}+    if med_parent > 0:+        dir_t *= med_parent / med_dir   # rescale so NET_BETA matches SHIFT_BETA units+    med2 = float(np.median(np.abs(dir_t[nz])))+    dir_t = np.where(np.abs(dir_t) > zmin_net * med2, dir_t, 0.0)   # sparse gate++    # per-type direction diversity: deviation of each type's dir from the type-mean dir+    tmean = dir_t.mean(axis=0, keepdims=True)+    dev = float(np.abs(dir_t - tmean).mean()) if typed else 0.0+    diag = {+        "n_tfs_kept": len(keep_tfs),+        "n_edges_used": int(n_edges),+        "n_genes_regulated": int((n_reg > 0).sum()),+        "entries_gated_kept": int((dir_t != 0).sum()),+        "median_abs_dir": med2,+        "mean_abs_dir_kept": float(np.abs(dir_t[dir_t != 0]).mean()) if (dir_t != 0).any() else 0.0,+        "type_deviation_mean_abs": dev,+    }+    return dir_t, diag++ def apply_gene_shift_typed(Xsel, dmus_t, inv_out, beta, zmin, max_shift):     from scipy import sparse as sp -    med = float(np.median(np.abs(dmus_t)))-    keep = np.abs(dmus_t) > zmin * med if med > 0 else np.zeros(dmus_t.shape, dtype=bool)+    absd = np.abs(dmus_t)+    med = float(np.median(absd))+    # med==0 only when >50% of entries are zero (sparse net-direction matrix, already pre-gated+    # inside net_direction): keep every nonzero. Dense parent dmu matrices take the original path.+    keep = absd > zmin * med if med > 0 else absd > 0     D = np.where(keep, np.clip(beta * dmus_t, -max_shift, max_shift), 0.0).astype(np.float32)     Xc = sp.csr_matrix(Xsel).copy()     row_of = np.repeat(np.asarray(inv_out), np.diff(Xc.indptr))@@ -515,22 +681,44 @@ def main() -> None:     n_inputs = len(manifest["inputs"])     print(f"[inputs] n_input_stages={n_inputs} shift_beta={shift_beta}", file=sys.stderr)     shift_mode = os.environ.get("VEC_SHIFT_MODE", "type")   # global | type (screening knob)+    net_beta = float(os.environ.get("VEC_NET_BETA", NET_BETA))   # 0 -> off control (parent dmu path)+    net_k = float(os.environ.get("VEC_NET_K", NET_K))+    net_zmin = float(os.environ.get("VEC_NET_ZMIN", NET_ZMIN))+    net_typed = os.environ.get("VEC_NET_MODE", "type") != "global"+    net_stack = os.environ.get("VEC_NET_STACK", "0") == "1"   # 1: parent typed dmu shift THEN net shift     if shift_beta != 0.0 and n_inputs >= 2:         entries = sorted(manifest["inputs"], key=lambda e: float(e["time"]))-        if shift_mode == "type":-            dmus_t = gene_delta_by_type(args.data, entries, genes, labels, inv, shift_k)-            Xsel, sdiag = apply_gene_shift_typed(Xsel, dmus_t, inv[idx], shift_beta, shift_zmin, shift_max)-            sdiag["mode"] = "type"-        else:-            dmus, dmu_raw, w = gene_delta(args.data, entries, genes, shift_k)-            Xsel, sdiag = apply_gene_shift(Xsel, dmus, shift_beta, shift_zmin, shift_max)-            pb_ref = pseudobulk(X)-            dp = pseudobulk(Xsel) - pb_ref-            cos = float(dp @ dmus / max(np.linalg.norm(dp) * np.linalg.norm(dmus), 1e-12))-            sdiag["dp_cosine_with_dmus"] = cos-            sdiag["n_dmus_nonzero"] = int((np.abs(dmu_raw) > 1e-9).sum())-            sdiag["mode"] = "global"-        print(f"[gene_shift] {sdiag}", file=sys.stderr)+        dirs_t, ndiag = None, {}+        if net_beta != 0.0:+            dirs_t, ndiag = net_direction(args.data, entries, genes, labels, net_k, net_zmin,+                                          typed=net_typed)+            if dirs_t is None:+                print(f"[gene_shift] net fallback to parent dmu: {ndiag}", file=sys.stderr)+        if net_beta == 0.0 or dirs_t is None or net_stack:+            if shift_mode == "type":+                dmus_t = gene_delta_by_type(args.data, entries, genes, labels, inv, shift_k)+                Xsel, sdiag = apply_gene_shift_typed(Xsel, dmus_t, inv[idx], shift_beta, shift_zmin, shift_max)+                sdiag["mode"] = "type"+            else:+                dmus, dmu_raw, w = gene_delta(args.data, entries, genes, shift_k)+                Xsel, sdiag = apply_gene_shift(Xsel, dmus, shift_beta, shift_zmin, shift_max)+                pb_ref = pseudobulk(X)+                dp = pseudobulk(Xsel) - pb_ref+                cos = float(dp @ dmus / max(np.linalg.norm(dp) * np.linalg.norm(dmus), 1e-12))+                sdiag["dp_cosine_with_dmus"] = cos+                sdiag["n_dmus_nonzero"] = int((np.abs(dmu_raw) > 1e-9).sum())+                sdiag["mode"] = "global"+            print(f"[gene_shift] {sdiag}", file=sys.stderr)+        if dirs_t is not None:+            if not net_typed and dirs_t.shape[0] == 1:+                Xsel, sdiag = apply_gene_shift(Xsel, dirs_t[0], net_beta, 0.0, shift_max)+            else:+                Xsel, sdiag = apply_gene_shift_typed(Xsel, dirs_t, inv[idx], net_beta, 0.0, shift_max)+            sdiag.update(ndiag)+            sdiag["mode"] = ("stack_" if net_stack else "") + (+                "collectri_net_typed" if net_typed else "collectri_net_global")+            sdiag["net_beta"] = net_beta+            print(f"[gene_shift] {sdiag}", file=sys.stderr)     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

完成。

**做了什么**：按 PLAN 在父节点（14）SHIFT 块内把位移方向来源从原始伪批量 Δμ 替换为 CollecTRI 网络传播方向（TF 活性变化 = 其靶基因 per-type Δμ 均值；dir_g = Σ sign×Δact；EB 收缩 K_net=5、重标定到父 β 单位、稀疏门控 ZMIN=1.5，只改已有非零位置）。缺文件/有效 TF<10 自动回退父路径；`VEC_NET_BETA=0` 为关闭对照。

**查分结果**（X3 A 半，seed 0；父 54.88 / 祖父 54.51）：β_net 扫描 1/2/3/4/5/6/8 → 54.68/54.94/55.20/**55.33**/55.07/54.95/54.78；出厂 β=4，**de_score 首次转正**（−0.026→+0.057，越过地板），代价是 mmd_u 回吐父节点的收益（0.0251→0.0270）。stack（Δμ+net）55.29 但 de_score 归零 → 不 stack。off control 与父逐字节一致；proxy 单输入块不激活、vec-check 两视图均 ok；用 12/20 次查分。

**下一步最值得试**：① net 方向与 Δμ 的融合而非叠加（如 dir 与 Δμ 符号一致才位移，或 dir+λ·Δμ 收缩组合），试图同时保住 mmd_u 和 de_score；② β=4 附近更细扫（3.5/4.5）+ 多种子确认峰不是 A 半量化运气；③ 把网络方向用于 recenter 锚定而非仅位移。

调研员

e) − module_t(earlier stage)，按细胞型分别计算（typed 模式）或全局计算。仅保留在两个阶段均检出 ≥10 个靶细胞的 TF。\n\n3. **方向传播**：对每个基因 g，direction_g = Σ_{t∈regulators(g)} sign(t→g) × Δactivity_t × confidence(t→g)。无调控边的基因 direction_g = 0（不位移）。\n\n4. **EB 收缩 + 稀疏门控**：w_g = n_regulators(g)/(n_regulators(g)+K_net)，K_net=5；收缩后 direction_shrunk_g = w_g × direction_g；门控 |direction_shrunk_g| > ZMIN_net × median(|direction_shrunk|)，ZMIN_net 初值 1.5，搜索 {1.0, 1.5, 2.0}。\n\n5. **施加位移**：x[i,g] += clip(β_net × direction_shrunk_g, ±MAX_SHIFT)，仅已有非零位置，clip≥0。β_net 初值 1.0，搜索 {0.5, 1.0, 2.0, 3.0}。MAX_SHIFT=1.0 不变。\n\n6. **诊断（在正式查分前）**：计算 direction_shrunk 向量与真实 dt（从 vec-score 返回的 score_parts 可推断方向）的秩相关；若 ρ < 0.05 则网络方向无信号，回退父配置。\n\n7. **单输入退路**：与父节点相同——只有 ≥2 输入阶段时激活，proxy10 不运行此块，输出逐字节一致。\n\n8. **筛选流程**：先在 X3 A 半 seed 0 跑 off control（β_net=0）确认与父一致；再跑 β_net=1 看 de_score 是否转正；若正则扫 β_net × ZMIN_net 小网格（≤6 组合）。用 vec-score 查 A 半，总查分 ≤8 次。",
  "expected_groups": ["de_recovery", "direction"],
  "risks": "1. CollecTRI 是泛型网络，X3 心脏特异转换（E8.75→E9.0）中关键 TF 活性变化可能太弱，传播方向无信号（诊断步骤的 ρ 检查可提前发现）。2. prior/ 中 CollecTRI 文件格式或基因命名与视图 panel 不匹配，导致有效边数过少（Engineer 应先统计交集基因数，若 <500 条边则信号不足，回退）。3. 方向正确但幅度不对，β 过大使 mmd_u/variogram 变差（父节点 global 模式的教训）；用 β≤3 + MAX_SHIFT=1.0 限制。4. 30 分钟内可能来不及完成全部筛选；优先级：off control → β=1 单次 → 若正则小网格。",
  "family_id": "composition_program",
  "mechanism": "用 CollecTRI TF→靶基因调控网络的边符号和 TF 活性变化传播出每基因的发育方向，替代噪声极大的原始伪批量 Δμ，作为型内表达程序位移的方向来源。",
  "vs_constant_shift": "方向由调控网络拓扑结构决定（每个基因的方向是其所有调控 TF 活性变化的加权符号和），不是每型一个常数向量；不同基因因调控边不同而获得不同方向和幅度；无调控边的基因不位移（稀疏性由网络结构天然约束）。",
  "mechanism_evidence": "1. direction_shrunk 向量与 dp（预测变化）的秩相关应显著为正（ρ>0.1）；2. 被位移的基因集合应与原始 Δμ 位移的集合有显著但不完全的重叠（Jaccard 0.2–0.7 说明网络提供了新信息）；3. X3 de_score 原始值从 −0.026 向正方向移动（目标 >0，即 skill>0.5）；4. de_direction 原始值从 0.0916 上升；5. 报告被位移的基因数、细胞数、型内方向离散度（不同型同一基因方向应不同）。",
  "mechanism_off_control": "设 VEC_NET_BETA=0（或等效环境变量）：方向传播块不施加任何位移，输出应与父节点 14 逐字节一致（h5 X/data 比较）。若 β=0 时输出不同，说明实现有 bug。另外，若 CollecTRI 文件缺失或有效边 <100，自动回退到父节点原始 Δμ 模式（typed β=1），保证不退化。",
  "sources": []
}

审查员

REVIEW.json written