总览 · ← 返回运行 20261003-093415-search-t1-r2-D-s1
节点 n9
| 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。 | 20261003-093415-search-t1-r2-D-s1 |
|---|---|
| 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。 | 18 分 |
| 程序版本 | — (programs.git) |
| 备注 | tune of #7: no_gain (query_cap); best gain 0.6344 vs min_gain 1 over 2 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 | 2 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 17,977 · 输出 2,065 · 思考 2,048 |
| 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 | 2 |
| 工具调用 | 共 0 次:— |
| 用时 | 1 分 |
| token 数 | 输入 17,977 · 输出 2,065 · 思考 2,048 |
| 任务(第一行) | — |
| 最后的回答(摘录) | Looking at the results from round 0: - SHIFT_A 1.0→1.5: X3 improved from 51.386 to 52.242 (+0.86), confirming the monotonic β trend continues - LAM 0.15→0.20: proxy10 improved from 64.109 to 64.300 (+0.19) - Overall: +0.634, still below the +1.0 threshold needed The X3 variogram at β=1.5 is 0.001482 (skill 0.518) — still above floor but declining. At β=2.0 it was 9.91/10 (below floor). So β=1.75 might be the sweet spot: more X3 gain while keeping variogram just above floor. For proxy10, the λ improvement is small. Let me try λ=0.25 to see if the trend continues. The weighted score formula is (proxy10 + 2×X3)/3. To gain +1.0 over parent (55.627), I need ≥56.627. Currently at 56.261, need +0.366 more. If β=1.75 gives X3 ~52.7 (interpolating between 52.24 at 1.5 and ~52.7 at 2.0 based on the original scan pattern), and λ=0.25 gives proxy10 ~64.4, then: (64.4 + 2×52.7)/3 ≈ 56.6. That's marginal. Let me try β=1.75 and keep λ=0.20 (since the λ gain is small and uncertain, don't over-tune two things at once on the proxy side). Actually, the variogram concern is real. At β=2.0, variogram was 9.91/10 (floor is 10.0, skill 0.5). At β=1.5 it's 10.37/20 wait no — it's 10.37/20.0 for variogram score out of 20. The skill is 0.518. The floor is skill 0.5. So there's only 0.018 skill margin. At β=1.75, it might drop to ~0.505 or below floor. Let me be more conservative: try β=1.6 instead. This gives a smaller step but safer on variogram. Alternatively, I could try a different approach: keep β=1.5 but tune other parameters that affect the overall score. Looking at the breakdown: - cell_state went from 58.06 to 60.00 (big gain from β increase, likely mmd_u improvement) - covariation dropped from 54.01 to 53.33 (variogram getting worse) - de_recovery improved from 52.05 to 52.64 The mmd_u on X3 improved significantly (0.03099→0.0289, skill 0.533→0.559). This is the main driver. Let me try β=1.75. Even if variogram drops slightly below the current level, as long as it stays above floor (skill 0.5), the mmd_u and de_score gains should compensate. The variogram at β=1.5 has skill 0.518, and at β=2.0 it was below floor. Linear interpolation suggests floor crossing around β≈1.85-1.9. So β=1.75 should be safe. CHANGE: SHIFT_A from 1.5 to 1.75 (continuing the monotonic X3 improvement trend; variogram at β=1.5 has skill 0.518 with floor at 0.5, so 1.75 should remain above floor while gaining more on mmd_u and de_score). <<<<<<< SEARCH SHIFT_A = 1.5 # two-stage per-type displacement: delta_g = SHIFT_A * (dt_out/dt_in) * (pb_last - pb_prev)_g; 0 disables ======= SHIFT_A = 1.75 # two-stage per-type displacement: delta_g = SHIFT_A * (dt_out/dt_in) * (pb_last - pb_prev)_g; 0 disables >>>>>>> REPLACE |
| 原始记录位置 | /home/spark-longxinyang/vec/runs/formal/20261003-093415-search-t1-r2-D-s1/nodes/9/tune/0/stream.jsonl 4 KB /home/spark-longxinyang/vec/runs/formal/20261003-093415-search-t1-r2-D-s1/nodes/9/tune/1/stream.jsonl 4 KB |