节点 n18

Let me analyze the results so far:

**Parent baseline:** 64.464 (need >65.464)
**Round 0:** 64.612 (W_DISP=0.0, SCALE_DAMP=0.9) — +0.148
**Round 1:** 64.655 (W_DISP=0.0, SCALE_DAMP=1.3) — +0.191

Round 1 is slightly better than round 0. The improvement from SCALE_DAMP=1.3 vs 0.9 is small (+0.043). Let me compare metrics:

| Metric | Parent (w=0.3, damp=1.0) | Round 0 (w=0, damp=0.9) | Round 1 (w=0, damp=1.3) |
|---|---|---|---|
| d2_shape | 0.05847 (0.436) | 0.04805 (0.486) | 0.04805 (0.486) |
| occupancy_dice | 0.8093 (0.507) | 0.8042 (0.492) | 0.8042 (0.492) |
| scale_log_ratio | 0.3944 (0.553) | 0.435 (0.529) | 0.4261 (0.534) |

Interesting: d2_shape and occupancy_dice are identical between round 0 and round 1! That means SCALE_DAMP only affects scale_log_ratio (uniform scaling doesn't change relative distances or spatial overlap patterns — it just changes the overall scale). So SCALE_DAMP only moves scale_log_ratio.

The ground truth scale_log_ratio seems to be somewhere around 0.42-0.43 based on the scores. Let me think: with SCALE_DAMP=1.3, scale_log_ratio=0.4261 (skill 0.534). With SCALE_DAMP=0.9, it's 0.435 (skill 0.529). With SCALE_DAMP=1.0 (base), it was 0.433.

The skill is slightly better at 1.3 (0.534) than at 0.9 (0.529). The direction is: higher SCALE_DAMP → lower scale_log_ratio → slightly better skill. So maybe I should push SCALE_DAMP even higher?

But the improvement is tiny (0.529 → 0.534 for a change of 0.4 in SCALE_DAMP). The score contribution from scale_log_ratio is 4.45/8.3 at SCALE_DAMP=1.3 vs 4.41/8.3 at SCALE_DAMP=0.9. That's only 0.04 points of skill improvement.

I'm stuck. The maximum improvement I've achieved is +0.191, and I need +1.0. The problem is that the base method is already near its ceiling for this proxy.

Let me think differently. What other parameters could I tune?

1. **n_out (cell count):** Currently `log_interp(A.n, B.n, t)` = interpolated between 58716 and 53742 at t=0.4, then clipped to max_cells=17616. Since the interpolated value is likely >17616, it's always clipped to max_cells. So this is fixed.

2. **Composition pseudocount (eps = 0.5/max(A.n, B.n)):** This is very small (~8.5e-6). Changing it might shift which cells get sampled, but the effect would be minimal.

3. **The t parameter:** Fixed by the bracket (0.4).

4. **stage_split logic:** The `n_b_target = int(round(t * n_out))` = round(0.4 * 17616) = 7046. Currently from_a=9342, from_b=8274. So from_b=8274 > 7046? Wait, that doesn't match. Let me re-read...

Actually, `n_b_target = int(round(t * n_out))` = round(0.4 * 17616) = 7046. But the log says from_b=8274. So the stage_split function isn't hitting the target exactly (due to pool constraints and the quota structure).

5. **What about the procrustes alignment?** The method uses `align_pair` with method="procrustes". Maybe a different alignment could help? But that might be "changing the method."

6. **What about trying SCALE_DAMP much higher, like 2.0 or 3.0?** Since the only effect is on scale_log_ratio, and the direction seems to be "higher is slightly better," maybe pushing it further would help.

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