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节点 n14

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。20261002-202908-search-t1-scr-D
父节点n10
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。4 分
程序版本— (programs.git)
备注tune of #10: no_gain (max_rounds); best gain -0.2205 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2 分
token 数?输入 / 输出(含思考)的 token 数,来自接口返回的用量。输入 21,748 · 输出 2,637 · 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2 分
token 数输入 21,748 · 输出 2,637 · 思考 3,072
任务(第一行)—
最后的回答(摘录)
I'll analyze the situation carefully.

**Current state:** Parent node baseline is 57.116. I need to gain >1 point. Round 0 (reg=0.1) failed (-0.375). Round 1 (winsorization) failed due to SEARCH block mismatch.

**What I know from the parent's notes:**
- κ=0.25 is on a flat plateau (0.2–0.3 → 56.6–56.9)
- covariation is the weakest component (dropped from ~47 to ~44 with OT)
- The winsorization idea was suggested but never tried
- reg=0.1 hurts slightly

**The key insight:** The per-cell correction `κ·scale·C_j` is added *after* the type-level displacement `df` is computed, but both go through `max(0,·)` clipping together. The issue is that `scale = α·r` multiplies the per-cell correction too. For X3, r=0.25 (target is 0.25 days after last input), so scale=1.75*0.25=0.4375. The per-cell correction magnitude is κ·scale·||C|| ≈ 0.25·0.4375·12.5 ≈ 1.37 per cell on average.

**A more promising approach:** Instead of winsorizing (which failed to match), let me try reducing `--ot-min` from 10 to 5. This would include more cell types in the OT correction (aSHF has 7 source cells currently marked "too-few"), giving more types per-cell diversity. But aSHF only has 9 output cells, so the impact is minimal.

**Better idea:** Try `--alpha` from 1.75 to 2.0. The parent node 8 tuned α=1.75, but that was *before* the OT correction was added. With the OT per-cell correction, the type-level displacement might benefit from being slightly stronger to maintain the covariation structure. But this is risky.

**Actually, the most promising untried change:** Lower `--ot-reg` to 0.02 or 0.03. Sharper OT coupling → more concentrated barycentric displacements → δ_j becomes more informative (less diffuse noise). The parent tried reg=0.1 (worse) but never tried *lower* reg. Lower reg makes the coupling more peaked, meaning each stage-2 cell is matched to fewer stage-1 cells, making the displacement more specific. This could improve both cell_state and potentially direction.

Let me try `--ot-reg` from 0.05 to 0.02.

CHANGE: Decreased --ot-reg from 0.05 to 0.02 (sharper entropic OT coupling produces more concentrated barycentric displacements, making per-cell corrections more specific and less noisy; reg=0.1 was worse but lower reg was never tried).

<<<<<<< SEARCH
    parser.add_argument("--ot-reg", type=float,
                        default=float(os.environ.get("VEC_OT_REG", "0.05")))
=======
    parser.add_argument("--ot-reg", type=float,
                        default=float(os.environ.get("VEC_OT_REG", "0.02")))
>>>>>>> REPLACE
原始记录位置/home/spark-longxinyang/vec/runs/formal/20261002-202908-search-t1-scr-D/nodes/14/tune/0/stream.jsonl 3 KB
/home/spark-longxinyang/vec/runs/formal/20261002-202908-search-t1-scr-D/nodes/14/tune/1/stream.jsonl 5 KB
/home/spark-longxinyang/vec/runs/formal/20261002-202908-search-t1-scr-D/nodes/14/tune/2/stream.jsonl 3 KB