节点 n7

Let me analyze the situation:

**Round 0 analysis:** MIN_CELLS 30→10 changed nothing because all 33 types already had ≥30 cells on the proxy view. Confirmed not the bottleneck.

**Round 1 analysis:** DRIFT_MULT 0.5→0.52 scored 51.965, worse than 52.067. The shape_scale dropped from 58.47 to 58.03. So 0.52 is slightly past the optimum.

**Current state:** DRIFT_MULT=0.5 is the parent's submitted value, giving 52.067. The grid shows 0.5 is the best tested value. DRIFT_MULT=0.52 is worse, DRIFT_MULT=0.48 was never tested.

**What to try now?** The parent already sits at the grid peak. To gain >1 point over the parent baseline (52.067), I need something different from just tweaking DRIFT_MULT.

Let me think about what else can be tuned:

1. **Per-type displacement weighting by cell count**: Types with very few cells have noisy centroid estimates. Weighting displacement by `min(1, n/threshold)` could reduce noise. But Round 0 showed all types have ≥30 cells, so this wouldn't change anything on proxy.

2. **Displacement magnitude capping**: Large displacements (Forebrain 406, PAM types ~320) might overshoot. Capping |d| per type could help. But this reduces the effective drift for the largest movers.

3. **SCALE_MULT**: The METHOD.md says the proxy shows contraction (RMS 354→217), so positive SCALE_MULT would contract further (wrong direction for real growth). Negative SCALE_MULT would expand, but was dismissed as "proxy-fitting trick." However, a very small negative SCALE_MULT could add a gentle expansion component that helps shape_scale without being extreme.

4. **Per-type displacement magnitude normalization**: Instead of using raw displacement vectors, normalize each type's displacement to a fixed magnitude (or cap it). This would prevent the largest movers from dominating.

Let me think about option 4 more carefully. The displacement magnitudes range from ~40 to ~407. If I normalize all displacements to have the same magnitude (e.g., the median), then small-movement types get boosted and large-movement types get damped. This is a structural change though, not just a parameter.

Actually, let me reconsider option 3. A small negative SCALE_MULT would expand the point cloud. The proxy went from RMS 354 to 217 (contraction). If the target is larger (E10.5 heart is bigger than E9.5), then the true direction is expansion. So a small negative SCALE_MULT (like -0.1) might help shape_scale by making the output cloud slightly larger.

Let me estimate: with SCALE_MULT=-0.1 and λ=1.5, factor = (217/354)^(-0.1*1.5) = (0.613)^(-0.15) = e^(0.15*0.489) = e^(0.0734) ≈ 1.076. So RMS would go from ~256 to ~276. That's a ~8% expansion.

Actually, this is a reasonable idea. The parent METHOD.md explicitly rejected it, but the reasoning was "no biological basis" — however, hearts do grow from E9.5 to E10.5. A gentle expansion is biologically motivated.

But wait — the task says "不要换方法，只调它的参数." SCALE_MULT is already a parameter in the code (defaulting to 0.0), so changing it from 0.0 to a small negative value is parameter tuning, not method change.

Let me try SCALE_MULT=-0.15. This gives factor = (217/354)^(-0.15*1.5) = (0.613)^(-0.225) = e^(0.225*0.489) = e^(0.110) ≈ 1.116. RMS ~256 → ~286. That might be too aggressive.

Let me be more conservative: SCALE_MULT=-0.1. factor = (0.613)^(-0.15) = e^(0.15*0.489) = e^(0.073) ≈ 1.076. RMS ~256 → ~275.

Actually, let me think about this differently. The shape_scale score at DRIFT_MULT=0.5 is 58.47 (seed average). The local_spatial is 49.80 (slightly below 50, meaning the drift slightly hurts local structure). 

What if I try a combination: keep DRIFT_MULT=0.5 but add a small negative SCALE_MULT to expand the cloud? The expansion would help shape_scale (moving toward the target scale) and might not hurt local_spatial much (since it's a uniform scaling around the centroid).

Let me try SCALE_MULT=-0.1, keeping DRIFT_MULT=0.5 and MIN_CELLS=30.

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