节点 n12

Let me analyze the results. Increasing α from 1.5 to 2.0 decreased the score by 1.117, with cell_state dropping from 80.63 to 77.47 and covariation from 45.25 to 43.69. So α=2.0 is too aggressive. The current code is back to the parent's run.py with α=1.5.

Let me think about other directions. The score components are:
- cell_state: 80.63 (main gain source)
- covariation: 45.25
- direction: 49.27
- de_recovery: 50.00

Since α=2.0 hurt, maybe I should try decreasing α slightly, or tune other parameters. Let me consider:

1. **γ tuning**: γ=10 was chosen, γ=12 was close (58.12 vs 58.20 two-seed). Maybe γ=9 or γ=11 could be slightly better, but unlikely to gain >1 point.

2. **clip tuning**: Current clip=2.0. With γ=10, f = 1+10*clip(p,-2,2). For cells with large negative p, f can go very negative, causing displacement reversal. Reducing clip to 1.5 would limit extreme modulation.

3. **EB noise coefficient**: The noise term is `0.5 * (v1+v2) * (1/n1 + 1/n2)`. Reducing the 0.5 factor would increase λ (more trust in observed differences), making displacements larger. This could help cell_state without changing the modulation structure.

4. **α slightly down**: α=1.3 might be better since the modulation already amplifies displacement for many cells.

Let me think about what's most promising. The key insight from the parent is that gains come from cell_state (spreading cells along trajectory). The modulation factor f has mean around 1 but with max(0,·) truncation, negative displacements get clipped. 

Actually, let me reconsider. With γ=10 and clip=2, for cells with p=-0.2 (about 1.3 std below mean), f = 1+10*(-0.2) = -1. These cells get displaced in the opposite direction, then clipped at 0. This effectively removes their signal. Maybe reducing the clip bound would help by preventing such extreme reversals.

Let me try **clip from 2.0 to 1.0**. This limits f to [1-10, 1+10] = [-9, 11] instead of [-19, 21]. With std(p)≈0.15, most cells have |p|<0.3, so f is in [-2, 4] anyway. But the extreme cells (|p|>0.5) would be less extreme. This might help covariation (less distortion of extreme cells) while maintaining most of the cell_state gain.

Actually, wait. Let me reconsider the direction. The parent notes say γ=16 started to hurt. The issue with very large modulation is that it distorts the covariance structure. Reducing clip could be a way to get the benefit of modulation without the extreme distortions.

But actually, I think a more promising direction is to try **reducing the noise coefficient in EB shrinkage**. Currently it's 0.5. If I reduce it to 0.3, λ increases, meaning we trust the observed per-type differences more, leading to larger displacements. This is different from increasing α because it's gene-specific: genes with clear signal get more displacement, noisy genes get less. This could improve both cell_state (better targeting) and potentially direction (more accurate per-gene displacement).

Let me try changing the noise coefficient from 0.5 to 0.3.

Looking at the code:
```python
noise = 0.5 * (v1 + v2) * (1.0 / n1 + 1.0 / n2)
```

Change to:
```python
noise = 0.3 * (v1 + v2) * (1.0 / n1 + 1.0 / n2)
```

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