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节点 n5 在终选来历上

分型 Sinkhorn OT 重心投影插值（T2HI-02）：mix 组成不变，共有类型细胞沿表达匹配的传输路径按 gamma·t 向对侧重心位移坐标；机制在代理上单调伤 shape/cell_state，最终取最温和配置 coords+gamma0.25，仍略低于父节点 mix。

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 488f9376ae: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 1206b86fb1。改动的文件：solution/METHOD.md +32 −0、solution/ot_interp.py +144 −0、solution/run.py +55 −13

diff --git a/solution/METHOD.md b/solution/METHOD.mdnew file mode 100644index 0000000..d4f73a6--- /dev/null+++ b/solution/METHOD.md@@ -0,0 +1,32 @@+分型 Sinkhorn OT 重心投影插值（T2HI-02）：mix 组成不变，共有类型细胞沿表达匹配的传输路径按 gamma·t 向对侧重心位移坐标；机制在代理上单调伤 shape/cell_state，最终取最温和配置 coords+gamma0.25，仍略低于父节点 mix。++## 方法++- 加载、procrustes 对齐（z 保持切片轴）、log 线性 RMS 缩放、细胞数与分层抽样组成全部与父节点 2（mix）逐位一致（同一 rng 序列，`mix_indices`）。+- 机制（`OT_ENABLED=True`，`solution/ot_interp.py`）：对每个在两侧括号阶段都出现、且两侧各 ≥5 个细胞的类型，用表达上的平方欧氏代价跑熵正则 Sinkhorn（eps=0.05×median(C)，60 次迭代，均匀边际，每侧上限 2500 细胞，其余细胞按表达最近邻借用子集的耦合行）。每个被抽中的输出细胞向其对侧耦合行的重心投影位移：coords_out=(1−gamma·t)·own+gamma·t·bary（side a；side b 对称）。+- 单侧独有类型原样通过（与父节点一致）。无括号（b 为 None）时走父节点的单阶段回退。+- 最终配置 `OT_PARAMS={eps_frac:0.05, iters:60, cap:2500, min_per_type:5, mode:"coords", gamma:0.25}`：只位移坐标，表达保留真实细胞（mode="both" 会把 cell_state 从 66.7 拉到 63-64，表达混合是主要伤害源）。++## 机制生效证据（proxy，E8.25_late+E9.5→E8.75，t=0.4，seed 0）++- 3553 个细胞（5 个共有类型 × 两侧）被位移；99.7% 的细胞位移向量与其类型平均位移之差 >10% 位移 RMS —— 是逐细胞位移，不是常数平移。+- 对照（`OT_ENABLED=False`，走父节点 `interpolate` 原路径）：输出 X 与坐标与父节点程序逐位相同（本地 diff 验证），共享加载/对齐路径无 bug。++## 查分（vec-score A 半，proxy；父节点 A 半 59.12）++| 配置 | skill | expr | state | shape | local |+|---|---:|---:|---:|---:|---:|+| both, gamma=1, eps=.05 | 56.38 | 63.9 | 63.1 | 47.3 | 51.3 |+| coords, gamma=1 | 57.55 | 63.8 | 65.9 | 47.3 | 53.2 |+| both, gamma=0.5 | 57.57 | 64.2 | 64.1 | 50.7 | 51.3 |+| both, gamma=0.25 | 58.49 | 64.0 | 64.8 | 52.5 | 52.6 |+| **coords, gamma=0.25（提交）** | **58.90** | 63.8 | 65.9 | 52.5 | 53.3 |+| coords, gamma=0.25, eps=.01 | 58.86 | 63.8 | 65.9 | 52.5 | 53.2 |++## 结论与未验证++- 机制在此代理括号上单调有害：任何 gamma>0 的位移都拉低 occupancy_dice / shape_scale 和 cell_state，gamma→0 单调收敛回父节点分。原因推测：E8.25↔E9.5 跨度大、只有 5 个共有类型参与对齐，procrustes 残差大，重心位移把细胞拉进错误占位区。+- 提交版本机制打开（coords, gamma=0.25），A 半 58.90，与父节点差 −0.2，在噪声（约 2 分）以内；如实报告：本方法族在代理上没有超过父节点 mix。+- 未验证：final 括号（E8.25+E8.75，31 个共有类型、对齐更准、t=0.5）上机制可能没那么有害——代理的结论不一定迁移，但本节点无 final 视图可测。+- 表达插值（mode="both"/"expr"）已验证为负收益，不建议后续再试。+- 生物学知识来源：无外部数据/prior 使用；只用了「同一细胞类型在两阶段间连续变形」这一通用发育连续性假设。diff --git a/solution/ot_interp.py b/solution/ot_interp.pynew file mode 100644index 0000000..5b2f083--- /dev/null+++ b/solution/ot_interp.py@@ -0,0 +1,144 @@+"""Per-type entropy-regularised OT (Sinkhorn) barycentric interpolation.++For every cell type present in both bracketing stages, cells of the two sides+are matched by an entropy-regularised transport plan computed on squared+Euclidean expression cost. Each output cell keeps its own side's value with+weight (1-t) / t and moves the rest of the way toward the barycentric+projection of its coupling row, in expression and in coordinates. Types that+exist on only one side (or with too few cells) pass through unchanged, exactly+as in the parent ``mix`` method.+"""++from __future__ import annotations++import numpy as np+from scipy.spatial.distance import cdist++EPS_FRAC = 0.05+ITERS = 60+CAP = 2500+MIN_PER_TYPE = 5+++def _sinkhorn_weights(C: np.ndarray, eps: float, iters: int) -> np.ndarray:+    """Row-normalised coupling rows W (n_a x n_b), W @ 1 = 1, uniform marginals."""+    na, nb = C.shape+    K = np.exp(-C / eps)+    K += 1e-300+    u = np.full(na, 1.0 / na)+    v = np.full(nb, 1.0 / nb)+    for _ in range(iters):+        u = 1.0 / (na * (K @ v) + 1e-300)+        v = 1.0 / (nb * (K.T @ u) + 1e-300)+    P = u[:, None] * K * v[None, :]+    rs = P.sum(axis=1, keepdims=True)+    rs[rs <= 0] = 1.0+    return P / rs+++def _bary_for_targets(X_side: np.ndarray, sub_idx: np.ndarray, W: np.ndarray,+                      targets_local: np.ndarray) -> np.ndarray:+    """Coupling rows (as weight matrices) for target cells of this type.++    ``targets_local`` are positions within the full per-type side matrix. Cells+    in the OT subset use their own row; the rest borrow the row of their+    nearest subset cell in expression space.+    """+    pos = -np.ones(X_side.shape[0], dtype=int)+    pos[sub_idx] = np.arange(sub_idx.size)+    rows = np.empty((targets_local.size, W.shape[1]))+    need = pos[targets_local] < 0+    have = ~need+    if have.any():+        rows[have] = W[pos[targets_local[have]]]+    if need.any():+        d = cdist(X_side[targets_local[need]], X_side[sub_idx], metric="sqeuclidean")+        rows[need] = W[d.argmin(axis=1)]+    return rows+++def ot_interpolate(xa_all: np.ndarray, xb_all: np.ndarray,+                   ca: np.ndarray, cb: np.ndarray,+                   labels_a: np.ndarray, labels_b: np.ndarray,+                   ia: np.ndarray, ib: np.ndarray, t: float,+                   rng: np.random.Generator,+                   eps_frac: float = EPS_FRAC, iters: int = ITERS,+                   cap: int = CAP, min_per_type: int = MIN_PER_TYPE,+                   mode: str = "both", gamma: float = 1.0):+    """Interpolate sampled cells (ia from side a, ib from side b).++    ``xa_all`` / ``xb_all`` are dense expression matrices (cells x genes),+    ``ca`` / ``cb`` the aligned, RMS-scaled coordinates of both full stages.+    Returns (expr, coords, evidence dict).+    """+    expr_out = np.empty((ia.size + ib.size, xa_all.shape[1]), dtype=np.float64)+    coords_out = np.empty((ia.size + ib.size, 3), dtype=np.float64)+    expr_out[:ia.size] = xa_all[ia]+    expr_out[ia.size:] = xb_all[ib]+    coords_out[:ia.size] = ca[ia]+    coords_out[ia.size:] = cb[ib]++    lab_a = np.asarray(labels_a).astype(str)+    lab_b = np.asarray(labels_b).astype(str)+    shared = sorted(set(lab_a[ia]) & set(lab_b[ib]))+    n_moved = 0+    disp_dev = []+    for typ in shared:+        rows_a = np.flatnonzero(lab_a == typ)+        rows_b = np.flatnonzero(lab_b == typ)+        if rows_a.size < min_per_type or rows_b.size < min_per_type:+            continue+        sub_a = rows_a if rows_a.size <= cap else np.sort(rng.choice(rows_a, cap, replace=False))+        sub_b = rows_b if rows_b.size <= cap else np.sort(rng.choice(rows_b, cap, replace=False))+        Xa = xa_all[sub_a]+        Xb = xb_all[sub_b]+        C = cdist(Xa, Xb, metric="sqeuclidean")+        med = float(np.median(C))+        eps = max(eps_frac * med, 1e-12)+        W = _sinkhorn_weights(C, eps, iters)++        # positions of sampled side-a cells of this type inside ia+        sel_a = np.flatnonzero(lab_a[ia] == typ)+        sel_b = np.flatnonzero(lab_b[ib] == typ)+        # local indices within the per-type full row arrays+        loc_a = np.searchsorted(rows_a, ia[sel_a])+        loc_b = np.searchsorted(rows_b, ib[sel_b])+        suba_loc = np.searchsorted(rows_a, sub_a)+        subb_loc = np.searchsorted(rows_b, sub_b)++        Wa = _bary_for_targets(xa_all[rows_a], suba_loc, W, loc_a)+        Wb = _bary_for_targets(xb_all[rows_b], subb_loc, W.T.copy(), loc_b)++        bary_x_a = Wa @ xb_all[sub_b]+        bary_p_a = Wa @ cb[sub_b]+        bary_x_b = Wb @ xa_all[sub_a]+        bary_p_b = Wb @ ca[sub_a]++        te = gamma * t if mode in ("both", "expr") else 0.0+        tc = gamma * t if mode in ("both", "coords") else 0.0+        if sel_a.size:+            expr_out[sel_a] = (1.0 - te) * xa_all[ia[sel_a]] + te * bary_x_a+            coords_out[sel_a] = (1.0 - tc) * ca[ia[sel_a]] + tc * bary_p_a+            d = coords_out[sel_a] - ca[ia[sel_a]]+            dm = d.mean(axis=0)+            rms = float(np.sqrt((d * d).sum(axis=1).mean())) + 1e-9+            disp_dev.append(float(np.mean(np.sqrt(((d - dm) ** 2).sum(axis=1)) > 0.1 * rms)))+            n_moved += sel_a.size+        if sel_b.size:+            k = sel_b + ia.size+            ue = gamma * (1.0 - t) if mode in ("both", "expr") else 0.0+            uc = gamma * (1.0 - t) if mode in ("both", "coords") else 0.0+            expr_out[k] = ue * bary_x_b + (1.0 - ue) * xb_all[ib[sel_b]]+            coords_out[k] = uc * bary_p_b + (1.0 - uc) * cb[ib[sel_b]]+            d = coords_out[k] - cb[ib[sel_b]]+            dm = d.mean(axis=0)+            rms = float(np.sqrt((d * d).sum(axis=1).mean())) + 1e-9+            disp_dev.append(float(np.mean(np.sqrt(((d - dm) ** 2).sum(axis=1)) > 0.1 * rms)))+            n_moved += sel_b.size++    ev = {+        "n_shared_types_moved": len(disp_dev),+        "n_cells_moved": int(n_moved),+        "frac_disp_dev_from_typemean": float(np.mean(disp_dev)) if disp_dev else 0.0,+    }+    return expr_out, coords_out, evdiff --git a/solution/run.py b/solution/run.pyindex 8c61767..5b07816 100644--- a/solution/run.py+++ b/solution/run.py@@ -1,13 +1,23 @@ #!/usr/bin/env python3-"""mix (T2 interpolation): real cells from both bracketing inputs, drawn (1−t, t).--Brackets the target with the nearest inputs before and after it, puts both in-one frame (``ALIGN``), rescales both clouds to the log-linear RMS-exp(log r_a + SCALE_DAMP·t·Δlog r), and draws cells stratified by type:-round(t·n) from the later stage, the rest from the earlier one. Expression and-coordinates travel together. n is log-linear in t, clipped to the board range.-Parameters are the T2 card's choice for this board (selected_params.json).-If the target is not bracketed, falls back to the latest input before it.+"""Per-type Sinkhorn OT barycentric interpolation (T2 interpolation).++Brackets the target with the nearest inputs before and after it, puts both+clouds in one frame (procrustes, z kept as slice axis), rescales both to the+log-linear RMS exp(log r_a + scale_damp*t*(log r_b - log r_a)), and draws+cells stratified by type: round(t*n) from the later stage, the rest from the+earlier one (same composition as the parent ``mix`` method).++Mechanism (OT_ENABLED=True): for every cell type present on both sides, an+entropy-regularised transport plan (Sinkhorn, eps = 0.05*median squared+Euclidean expression cost, 60 iterations, uniform marginals, per-side cap+2500 cells with nearest-expression borrowing for the rest) matches cells+between the stages. Each sampled cell then moves to+(1-t)*own + t*barycentric projection of its coupling row, in expression AND+in coordinates, so output cells are genuine intermediates instead of a binary+mixture of endpoint cells. Types on one side only pass through unchanged.++Control (OT_ENABLED=False): identical code path to parent node 2 (``mix``),+bit-identical output. """  from __future__ import annotations@@ -18,11 +28,17 @@ import sys  import numpy as np -from src.task2_spatial.methods import interpolate-from src.task2_spatial.sample import take+from src.task2_spatial.frame import log_interp, rms_radius, scale_to_rms, align_pair+from src.task2_spatial.methods import _jitter, _limits, interpolate+from src.task2_spatial.sample import mix_indices, take+from src.task2_spatial.transport import as_dense from src.task2_spatial.view_io import board_params, interp_bracket, load_manifest, panel_genes, read_stage, write_t2 +from ot_interp import ot_interpolate++OT_ENABLED = True PARAMS = {"align": "procrustes", "scale_damp": 1.0}+OT_PARAMS = {"eps_frac": 0.05, "iters": 60, "cap": 2500, "min_per_type": 5, "mode": "coords", "gamma": 0.25}   def main() -> None:@@ -44,9 +60,35 @@ def main() -> None:     stage_a = read_stage(args.data, a, genes)     stage_b = read_stage(args.data, b, genes)     params = board_params(manifest, "mix", PARAMS, args.seed)-    expr, coords, info = interpolate(stage_a, stage_b, t, params)++    if not OT_ENABLED:+        expr, coords, info = interpolate(stage_a, stage_b, t, params)+        ev = {"ot": False}+    else:+        t = float(t)+        rng = np.random.default_rng(int(params["seed"]))+        aligned_a, aligned_b, info = align_pair(+            stage_a.coords, stage_b.coords, stage_a.labels, stage_b.labels, str(params["align"]))+        rms_a = rms_radius(stage_a.coords)+        rms_b = rms_radius(stage_b.coords)+        target_rms = log_interp(rms_a, rms_b, t, float(params["scale_damp"]))+        ca = scale_to_rms(aligned_a, target_rms)+        cb = scale_to_rms(aligned_b, target_rms)+        n = _limits(params, stage_a.n, stage_b.n, t, "interp")+        ia, ib = mix_indices(stage_a.labels, stage_b.labels, t, n, rng)+        xa_all = np.asarray(stage_a.X.todense(), dtype=np.float64)+        xb_all = np.asarray(stage_b.X.todense(), dtype=np.float64)+        expr, coords, ev = ot_interpolate(+            xa_all, xb_all, ca, cb, stage_a.labels, stage_b.labels, ia, ib, t, rng, **OT_PARAMS)+        expr = np.clip(expr, 0.0, None).astype(np.float32)+        coords = _jitter(coords, rng)+        coords = scale_to_rms(coords, target_rms).astype(np.float32)+        info.update(t=t, n=int(expr.shape[0]), rms_a=rms_a, rms_b=rms_b,+                    out_rms=rms_radius(coords), n_from_a=int(ia.size), n_from_b=int(ib.size))+        ev["ot"] = True+     keep = {k: info.get(k) for k in ("t", "n", "rms_a", "rms_b", "out_rms", "n_shared_types", "z_dot", "z_flipped", "align")}-    print(json.dumps({"bracket": [a["stage"], b["stage"]], **keep}, default=float), file=sys.stderr)+    print(json.dumps({"bracket": [a["stage"], b["stage"]], **keep, **ev}, default=float), file=sys.stderr)     write_t2(args.out, expr, coords, genes, seed=args.seed)  

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

**做了什么**：按 PLAN 实现 T2HI-02（分型 Sinkhorn OT 重心投影插值，`solution/ot_interp.py`），mix 组成/对齐/缩放与父节点逐位一致；`OT_ENABLED=False` 对照验证与父节点输出 bit-identical。机制证据：3553 个细胞被逐细胞位移，99.7% 位移偏离类型均值 >10% RMS。

**查分**（8 次，A 半；父 59.12）：both γ=1 → 56.38；coords γ=1 → 57.55；both γ=0.5 → 57.57；both γ=0.25 → 58.49；**coords γ=0.25（提交）→ 58.90**；eps=0.01 → 58.86。机制在代理上单调有害（伤 occupancy/shape/cell_state），γ→0 收敛回父节点；提交版与父差 −0.2，在噪声内，METHOD.md 已如实写明。

**下一步最值得试**：代理只有 5 个共有类型、跨度大导致对齐残差是位移失败的根因——final 括号（31 个共有类型、t=0.5）对齐更准，机制伤害可能更小甚至反转；或改走「组成插值 + 类型内表达插值」路线攻 cell_state（胚榜审查意见同款），而不是坐标位移。

调研员

tric projection can collapse variance if coupling is too diffuse (high epsilon) → monitor output RMS; if it drops >20% below target, reduce epsilon. 3. 30-min budget is tight → Engineer should implement Sinkhorn as a standalone function first, test on a 50×50 toy case, then integrate. 4. Improvement may be <1 pt (noise level) → query at least 3 seeds before concluding.",
  "family_id": "T2HI-02",
  "mechanism": "Per-type entropy-regularised OT (Sinkhorn) matches cells between bracketing stages by expression similarity; barycentric projection then interpolates each cell's coordinates and expression along the matched transport path, producing cell-specific displacements that preserve local neighbourhood topology.",
  "vs_constant_shift": "Constant shift moves every cell of a type by the same vector (type centroid displacement). Here each cell receives a unique displacement determined by its expression-weighted OT coupling to cells in the other stage. Two cells of the same type but different expression profiles get different displacement vectors, so within-type spatial variance and local neighbourhood ordering are preserved rather than rigidly translated.",
  "mechanism_evidence": "1. Compare displacement vectors within a type: std of per-cell displacements should be >0 (not a single constant vector). 2. Report mean pairwise Spearman correlation of displacement vectors with expression similarity within type (should be positive). 3. Check local_spatial and shape_scale sub-scores individually; both should rise while cell_state stays ≥66. 4. Print fraction of cells whose displacement differs from the type-mean displacement by >10% of RMS; expect >50%.",
  "mechanism_off_control": "Set a flag OT_ENABLED=False in PARAMS. When False, skip Sinkhorn and fall back to parent's stratified sampling (identical code path to node 2). Output should be bit-identical to parent node 2's prediction. If scores differ, there is a bug in the shared loading/alignment path.",
  "sources": []
}