节点 n22

Looking at the results:
- Round 0 (COORD_MODE="interp"): 65.227 — worse than baseline 66.296 (-1.069)
- Round 1 (same change repeated): 65.227 — same result

The "interp" coordinate mode hurt both shape_scale (71.93→68.71) and local_spatial (63.66→62.61). So I need to revert to "hold" and try something else.

Let me think about what could genuinely improve the score by >1 point. The key observations:
1. shape_scale is already well-optimized at 71.93 with K=0.62
2. expression_change (64.77) and cell_state (64.82) are determined by the blend coefficient t and composition interpolation
3. local_spatial (63.66) is the weakest and wasn't improved by coordinate interpolation

Let me consider **KNN_K**. Currently 25. This controls cluster contiguity in `cluster_take`. Higher KNN_K means each cell sees more neighbors during region growth, producing more spatially compact clusters. More compact clusters → better preserved local neighborhood structure → potentially better local_spatial scores.

Alternatively, **N_CLUSTERS** = 10. Fewer clusters means larger contiguous blocks, which might preserve neighborhood structure better. Or more clusters means finer-grained spatial sampling.

Actually, let me think about this differently. The `cluster_take` function samples spatially contiguous blocks. With KNN_K=25 and N_CLUSTERS=10, we get 10 clusters each grown using 25-nearest-neighbor expansion. If I increase KNN_K to 50, clusters can grow more freely and might be more spatially coherent. If I decrease N_CLUSTERS to 5, each cluster is larger and more contiguous.

But wait — the key insight is that local_spatial measures how well the neighborhood structure of the output matches ground truth. The cluster sampling determines which cells are selected, and their spatial arrangement determines neighborhood preservation. With "hold" coordinates, the spatial positions are fixed (from the aligned+scaled input). So local_spatial depends on which cells are sampled and their relative positions.

Let me try increasing KNN_K from 25 to 50. This gives each frontier cell more neighbors to expand into during region growth, potentially creating more spatially coherent clusters that better preserve local neighborhood relationships.

CHANGE: Revert COORD_MODE to "hold" (round 0/1 showed "interp" hurts), and increase KNN_K from 25 to 50 for more spatially coherent cluster growth, targeting local_spatial improvement.

<<<<<<< SEARCH
COORD_MODE = os.environ.get("T2HI_COORD", "hold")  # interp | hold
=======
COORD_MODE = os.environ.get("T2HI_COORD", "hold")  # interp | hold
>>>>>>> REPLACE

Wait, COORD_MODE is already "hold" in the current file (the system said "当前程序是父节点的 run.py"). Let me re-read... Yes, the current program already has `"hold"` as default. So I just need to change KNN_K.

CHANGE: KNN_K from 25 to 50 — larger neighbor radius in cluster growth produces more spatially coherent sampled blocks, potentially improving local_spatial (weakest group at 63.66).

<<<<<<< SEARCH
KNN_K = int(os.environ.get("T2_KNN_K", "25"))
=======
KNN_K = int(os.environ.get("T2_KNN_K", "50"))
>>>>>>> REPLACE