节点 n12

I'll analyze the results from Round 1 and determine the best next parameter adjustment.

**Round 1 results analysis:**
- eps=0.0002 gave 65.391 (+0.585 over parent baseline 64.806)
- Improvements: cell_state +1.71, local_spatial +2.29, mmd_u skill improved
- Declines: expression_change -0.74, shape_scale -0.93, de_score dropped
- The shape_scale decline is concerning but may be noise

**Strategy for Round 2:**
The biggest gains came from cell_state and local_spatial. The de_score raw dropped from 0.5435→0.5109, suggesting expression interpolation quality slightly degraded. Let me try a different angle: the `scale_damp` parameter is currently 1.0 (full scaling to target RMS). Damping it slightly might preserve more spatial structure, helping shape_scale without hurting the composition benefits. However, looking at the code, `scale_damp=1.0` is hardcoded in the `log_interp` call.

Alternatively, I can try eps=0.0003 (between 2e-4 and 5e-4) to see if there's a sweet spot, or try adjusting the coordinate rescaling after mixing. 

Looking more carefully: after mixing, `coords = scale_to_rms(coords, target_rms)` is applied again. This double-scaling (first each side is scaled to target_rms, then the combined output is scaled again) might be slightly distorting the spatial structure. The second scaling should be nearly a no-op if both sides were already at target_rms, but the mixing of cells from two differently-shaped clouds could shift the RMS slightly.

Actually, the most promising direction: try `scale_damp` from 1.0 to something like 0.8 or 0.9. This would partially scale the coordinates toward the target RMS rather than fully, potentially preserving more of the original spatial relationships and helping shape_scale metrics. But this is hardcoded as `1.0` in two places.

Let me try changing the post-mix rescaling to use a damped factor (0.9) instead of full rescaling, which should help preserve spatial structure:

CHANGE: 混合后坐标重缩放从 scale_to_rms(coords, target_rms) 改为 scale_to_rms(coords, log_interp(rms_radius(coords), target_rms, 1.0, 0.85))，即只向目标 RMS 移动 85%（damp=0.85），减少过度缩放对空间结构的破坏，预期改善 shape_scale 和 local_spatial。

Actually, let me reconsider. The simpler and more defensible change: the `log_interp` call for target_rms uses `scale_damp=1.0`. If I reduce this to 0.9, the target RMS itself becomes closer to the geometric mean of the two input RMS values, which is a gentler interpolation. This affects both the per-side scaling and the final rescaling.

But actually, looking at the code more carefully, the most impactful single change would be to try a slightly different eps within the plateau. The grid showed 2e-4=65.35, 5e-4=64.98, 1e-3=65.15. Round 1 with 2e-4 gave 65.39. Let me try 3e-4 as a middle ground, or better yet, try 1e-4 to see if the improvement continues below the tested grid.

Given the plateau was 2e-4 to 1e-3, and 2e-4 was the best tested point, going to 1e-4 is extrapolation and risky. Instead, let me try a different parameter: the coordinate jitter noise scale. Currently it's `1e-4 * rms`. Increasing it slightly might help neighborhood_mmd by breaking exact coordinate ties more aggressively, but it's already very small.

Better idea: try changing the post-mix rescaling damp factor. Currently the code does `scale_to_rms(coords, target_rms)` after mixing. If instead I damp this to 0.95, it preserves slightly more of the mixed spatial structure:

CHANGE: 混合后坐标的 scale_to_rms 从完全缩放到 target_rms 改为 damp=0.9（即只缩放 90% 的距离到目标），保留更多混合后的空间结构，预期改善 shape_scale 和 neighborhood_mmd。

<<<<<<< SEARCH
    coords = _jitter(np.vstack(coord_parts), rng)
    coords = scale_to_rms(coords, target_rms)
=======
    coords = _jitter(np.vstack(coord_parts), rng)
    mixed_rms = rms_radius(coords)
    damped_rms = np.exp(np.log(mixed_rms) + 0.9 * (np.log(target_rms) - np.log(mixed_rms)))
    coords = scale_to_rms(coords, damped_rms)
>>>>>>> REPLACE