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节点 n18

manifold_ode draft: PCA-space neural ODE with kNN manifold constraint, Sinkhorn training

运行?一次完整的自动搜索或 Agent 会话,有自己的锁定配置和证据包。20261002-202908-search-t1-scr-D
父节点(种子,没有父节点)
子节点—
操作?种子:人写的起点;改进:在父节点上改;草稿:从头写;修复:修父节点的报错。草稿
状态生成失败
分数没有分数
审查未审查
用时?从运行开始到结束(或到现在)的挂钟时间。31 分
程序版本— (programs.git)
备注missing or empty solution/METHOD.md (method and knowledge sources are required)

方法说明?节点程序自带的 METHOD.md:这个程序做了什么、为什么。

没有 METHOD.md。

调研员的计划

名称manifold_ode draft: PCA-space neural ODE with kNN manifold constraint, Sinkhorn training
动机Draft node for family manifold_ode. Best tree score is 61.27 (node 13). Previous continuous-dynamics attempts failed: node 6 (OT-CFM) gen_failed, node 15 (VAE latent) scored only 49.05. The direction library notes that with only one observed interval (E8.5→E9.5) the field is weakly identified, and the official autonomous ODE lost to constant shift. This draft tests whether a non-autonomous, manifold-constrained ODE with Sinkhorn distribution-matching loss can capture curvature that straight-line per-type displacements (nodes 4/5/13) miss, particularly for the covariation group (currently 44.54, weakest among top nodes) and direction group (50.12, near noise floor).
做法1) Representation: take the union of cells from both input stages, select top-3000 HVGs by variance, compute PCA to 25 dims (consistent with nodes 5/13). Store the linear decoder (components_ matrix) and per-cell residuals.
2) Network: small MLP velocity field v(x,t): input [x(25d), t(1d)] → 64 → 64 → 25, tanh activations. ~4k params.
3) Training (≤3 min on GPU or 5 min CPU): subsample 2500 cells per stage. For each mini-batch (256 cells per stage), integrate E8.5 subset forward t=0→1 with 5 fixed Euler steps via torchdiffeq.odeint (or manual Euler loop). Loss = geomloss SamplesLoss('sinkhorn', p=2, blur=0.05)(pushed_E85, real_E95) + 0.1*kinetic_energy + λ_manifold * L_manifold. Train 300 iterations, Adam lr=1e-3.
4) Manifold constraint L_manifold: for each pushed cell at each Euler step, compute its k=10 nearest neighbors in the pooled (E8.5+E9.5) training PCA cloud; penalize velocity components orthogonal to the local tangent plane (project velocity onto top-3 local PCA of neighbors, penalize residual norm). λ_manifold=1.0 initial, search {0, 0.5, 1.0, 2.0}.
5) Extrapolation: integrate E9.5 cells forward t=1→2 with same field, 5 Euler steps, multiplied by damping factor α∈{0.5, …
风险1) With one interval the ODE cannot identify curvature; extrapolation to t=2 may just produce a noisy version of constant shift → Engineer should check if λ_manifold=0 and λ_manifold>0 give nearly identical outputs (mechanism not running). 2) Training may not converge in 300 iterations → check Sinkhorn loss curve; if still >50% of initial, increase to 500 iters or reduce blur. 3) Manifold constraint may over-smooth and wash out cell-type differences → monitor cell_state group; if it drops below 60, reduce λ_manifold. 4) Extrapolation drift at t=2 → if output variance >> input variance, reduce α to 0.5. 5) torchdiffeq import issues → fall back to manual Euler loop (5 lines). 6) Total time: representation 2min + training 3-5min + integration 1min + decode 1min + scoring 2min ≈ 10-12 min, well within 30 min.

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。

这个节点没有程序版本(没有生成代码)

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

编号标题出处
k032Neural ODE population dynamics from snapshots: TrajectoryNet, PRESCIENT, MIOFlow, scNODEarXiv:2002.04461 (TrajectoryNet); 10.1038/s41467-021-23518-w (PRESCIENT); arXiv:2206.14928 (MIOFlow); 10.1093/bioinformatics/btae393 (scNODE)
k031Offline OT toolkit in the sandbox: moscot TemporalProblem, wot OTModel, POT, geomloss10.1038/s41586-024-08453-2 (moscot); 10.1016/j.cell.2019.01.006 (Waddington-OT)
k034Flow matching and Schrödinger bridges offline: torchcfm (OT-CFM, SF2M), metric FM, DSBarXiv:2302.00482 (OT-CFM, Tong et al.); arXiv:2307.03672 ([SF]2M); arXiv:2405.14780 (metric flow matching); arXiv:2106.01357 (DSB)

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。

没有分析结果(ANALYSIS.json)。

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。

工程师

角色?调研员写计划、工程师改代码、分析员解读分数、审查员检查作弊。工程师 alibaba-token-plan-cn/qwen3.8-max
调用次数1 次(失败 1 次)
轮数?大模型一共回复了几轮(每轮可以调用多个工具)。16
工具调用?大模型调用读文件、执行命令等工具的次数,按工具名统计。共 20 次:bash 16、edit 3、write 1
用时30 分
token 数?输入 / 输出(含思考)的 token 数,来自接口返回的用量。输入 37,286 · 输出 8,081 · 思考 7,286
任务(第一行)按计划修改程序:manifold_ode draft: PCA-space neural ODE with kNN manifold constraint, Sinkhorn training
最后的回答(摘录)
α smaller is better monotonically — field underfits (sinkhorn only −10%). One try with larger blur + lr, plus λ=0 control:
原始记录位置?完整对话记录在 Spark 上的文件路径;看板不复制原始内容。/home/spark-longxinyang/vec/runs/formal/20261002-202908-search-t1-scr-D/nodes/18/engineer.jsonl 97 KB
/home/spark-longxinyang/vec/runs/formal/20261002-202908-search-t1-scr-D/nodes/18/engineer.stderr

调研员

角色调研员 alibaba-token-plan-cn/qwen3.8-max
调用次数1 次
轮数1
工具调用共 0 次:—
用时1 分
token 数输入 11,637 · 输出 1,519 · 思考 1,083
任务(第一行)为节点 n18(草稿,父节点 nNone)写改进计划 PLAN.json,思路 manifold_ode
最后的回答(摘录)
m PCA space trained with Sinkhorn divergence to transport E8.5→E9.5, then integrated forward one more day from E9.5; a kNN-based tangent-plane constraint penalizes velocity components leaving the local data manifold, forcing trajectories to bend along observed expression geometry rather than taking straight-line paths.",
 "vs_constant_shift": "Constant per-type displacement (nodes 4/5/13) applies the same vector to all cells of a type regardless of position. This ODE learns a position-dependent velocity field: cells at different locations in PCA space receive different velocities, and the manifold constraint ensures these velocities follow the curved data geometry. The displacement is not a fixed vector but the integral of a state-and-time-dependent field along a curved path.",
 "mechanism_evidence": "Engineer should report: (1) per-cell displacement vectors from ODE vs. straight-line (barycentric) displacement — compute cosine similarity distribution; if all >0.99 the manifold constraint did nothing. (2) Distribution of displacement magnitudes within each cell type — constant shift gives zero variance, ODE gives nonzero. (3) Four group scores separately for λ_manifold>0 vs λ_manifold=0. (4) Fraction of cells whose trajectory (intermediate Euler steps) deviates >1 PCA unit from the straight line connecting start and end points. (5) Sinkhorn loss before/after training to confirm the field learned meaningful transport.",
 "mechanism_off_control": "Same code, same architecture, same training budget, but set λ_manifold=0 (remove the tangent-plane penalty term from the loss). The ODE is then trained purely on Sinkhorn + kinetic energy without any manifold constraint. If outputs are element-wise identical (or cosine sim >0.999 per cell), the manifold constraint mechanism did not operate. Additionally, a second control: set damping α=0, which makes the output equal to stage-2 input (copy_last equivalent), confirming the ODE integration path is active.",
 "sources": []}
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原始记录位置/home/spark-longxinyang/vec/runs/formal/20261002-202908-search-t1-scr-D/nodes/18/researcher.jsonl 7 KB
/home/spark-longxinyang/vec/runs/formal/20261002-202908-search-t1-scr-D/nodes/18/researcher.stderr