Virtual Embryo Challenge更新于 10-03 19:58(北京时间) / 每 5 分钟更新

总览 · ← 返回运行 20261003-171955-search-t2-embryo-interp-chain-12h

节点 n18

运行?一次完整的自动搜索或 Agent 会话,有自己的锁定配置和证据包。20261003-171955-search-t2-embryo-interp-chain-12h
父节点n7
子节点—
操作?种子:人写的起点;改进:在父节点上改;草稿:从头写;修复:修父节点的报错。调参
状态没有改动
分数没有分数
审查未审查
用时?从运行开始到结束(或到现在)的挂钟时间。2 分
程序版本— (programs.git)
导入自20261003-094242-search-t2-embryo-interp-g24-D-s2#18
备注tune of #7: no_gain (max_rounds); best gain -0.0257 vs min_gain 1 over 3 round(s)

方法说明?节点程序自带的 METHOD.md:这个程序做了什么、为什么。

没有 METHOD.md。

调研员的计划

没有计划(PLAN.json)。

代码改动?这个节点的程序和父节点程序的逐行差别:绿色是新增,红色是删除。

这个节点没有程序版本(没有生成代码)

调研来源?调研员查到并用到的知识条目和文献检索结果(只列标题和编号)。

没有记录调研来源。

分析结果?分析员写的 ANALYSIS.json:改了什么、各组分数怎么变、假设是否成立、经验和下一步建议。

没有分析结果(ANALYSIS.json)。

对话摘要?每个角色和大模型对话的统计:轮数、工具调用、用时、token 数和最后的回答摘录;原始记录只给路径。

只给统计和最后回答的摘录;完整对话请到原始记录位置里列出的文件看。

tune

角色?调研员写计划、工程师改代码、分析员解读分数、审查员检查作弊。tune alibaba-token-plan-cn/qwen3.8-max
调用次数3 次
轮数?大模型一共回复了几轮(每轮可以调用多个工具)。0
工具调用?大模型调用读文件、执行命令等工具的次数,按工具名统计。共 0 次:—
用时1 分
token 数?输入 / 输出(含思考)的 token 数,来自接口返回的用量。输入 23,008 · 输出 1,654 · 思考 3,072
任务(第一行)—
最后的回答(摘录)—
原始记录位置?完整对话记录在 Spark 上的文件路径;看板不复制原始内容。—

stream

角色stream
调用次数0 次
轮数3
工具调用共 0 次:—
用时1 分
token 数输入 23,008 · 输出 1,654 · 思考 3,072
任务(第一行)—
最后的回答(摘录)
Looking at the results from round 0:
- `base_gain=1.3` gave 61.823 vs baseline 61.857 — slightly worse (-0.034).
- expression_change went 55.39→55.48 (+0.09) but cell_state dropped 54.21→54.13 (-0.08), and local_spatial dropped 52.28→52.13 (-0.15), neighborhood_mmd skill dropped 0.523→0.521.

So increasing base_gain helps expression_change slightly but hurts other groups. The net is negative. I should try the opposite direction or a different parameter.

Let me try `aniso_damp=2.2` instead of 2.5. From the grid: 2.2 gives 61.21 on seed 0 vs 2.5 gives 61.26. That's worse on seed 0, but the multi-seed behavior might differ. Actually, the baseline (61.857) is already the multi-seed average at damp=2.5, so moving to 2.2 would likely be worse.

Better idea: try `T2_SPREAD` from 0.10 to 0.12. The spread parameter controls the non-uniform (position-proportional) part of the expression shift. Since cell_state (54.21) measures how well individual cell states match, a slightly stronger spread might better differentiate cells along the trajectory. But this is risky.

Actually, let me reconsider `scale_damp`. Currently 0.5. The scale_log_ratio skill is 0.924 — already high but not maxed. If I try scale_damp=0.4 or 0.6, it might improve scale_log_ratio slightly. But the METHOD.md says 0.5 is optimal.

Let me try `aniso_damp=2.3` — between 2.2 and 2.5, slightly more conservative. The grid shows 2.2→61.21, 2.5→61.26 on seed 0. The difference is tiny (0.05), well within noise. But on multi-seed, 2.3 might be slightly more stable since it's less likely to overshoot on the final view (γ=2.3×1/3=0.767 vs 0.833).

Actually, a more promising direction: try `T2_SPREAD=0.12`. The spread controls per-cell heterogeneity in the expression advance. A slightly higher spread could improve cell_state by better matching individual cell trajectories. The expression_change group already responded positively to base_gain increase, suggesting there's room in the expression direction.

Let me try `spread=0.12` (from 0.10):

CHANGE: Increase T2_SPREAD from 0.10 to 0.12 to strengthen position-proportional expression heterogeneity, targeting cell_state improvement.

<<<<<<< SEARCH
    spread = 0.0 if mech_off else _env_float("T2_SPREAD", 0.10)
=======
    spread = 0.0 if mech_off else _env_float("T2_SPREAD", 0.12)
>>>>>>> REPLACE
原始记录位置/home/spark-longxinyang/vec/runs/formal/20261003-094242-search-t2-embryo-interp-g24-D-s2/nodes/18/tune/0/stream.jsonl 4 KB
/home/spark-longxinyang/vec/runs/formal/20261003-094242-search-t2-embryo-interp-g24-D-s2/nodes/18/tune/1/stream.jsonl 1 KB
/home/spark-longxinyang/vec/runs/formal/20261003-094242-search-t2-embryo-interp-g24-D-s2/nodes/18/tune/2/stream.jsonl 3 KB