| |
| """Render the summary into the markdown tables the report embeds. |
| |
| Kept separate from `run_analysis.py` so the numbers and their presentation |
| can be regenerated independently: re-run this after any change to the |
| feature extractors and the report's tables update without hand-editing. |
| |
| python analysis/make_tables.py # per-agent tables into analysis/out/ and docs/results/ |
| """ |
|
|
| from __future__ import annotations |
|
|
| import os |
| import sys |
|
|
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) |
|
|
| from run_analysis import AGENT_ROOTS, OUT_DIR, ROOT, build, summarize |
| from trace_analysis.probes import probe_cohort |
| from trace_analysis.stats import within_task_z |
|
|
|
|
| DOC_NAMES = { |
| "claude": ("claude_code_agent_trace_tables_2026-10.md", "Claude Code (claude-opus-5)", "claude_code_agent_trace_analysis_2026-10.md"), |
| "codex": ("codex_sdk_agent_trace_tables_2026-10.md", "Codex SDK (gpt-5.6-sol)", "codex_sdk_agent_trace_analysis_2026-10.md"), |
| } |
|
|
| DOC_HEADER = """# 附录:{title} Trace 逐项数据表(2026-10) |
| |
| 本文件由 `python analysis/make_tables.py` 生成,配合 |
| [`{doc}`]({doc}) 阅读。 |
| 修改特征抽取后重跑该脚本即可刷新,不要手工编辑表格内容。 |
| |
| ## 高层要点 / Takeaways |
| |
| 1. **这是附录,不是结论。** 这里只有逐 run、逐特征的**全量数值**, |
| 解读一律在 [`{doc}`]({doc}) 里,两边不重复。 |
| 2. **要查一个具体的 run 发生了什么**,从这里的表定位到 run id, |
| 再去 `analysis/out/*/narratives/` 读那一份的可读流水账。 |
| 3. **跨 benchmark 不要直接比绝对分** —— 用 `z in task` 那一列(同 benchmark 内的 z-score), |
| 本项目所有相关性分析都基于它。 |
| 4. **`cohort` 列决定一行算不算数**:只有 `primary` 和 `recovered` 进入分析, |
| 其余是被我们自己的基础设施掐断的,不是 agent 放弃。 |
| |
| > This file is a generated appendix: per-run, per-feature raw numbers only. All interpretation |
| > lives in the analysis document it accompanies. Compare across benchmarks using the `z in task` |
| > column, never the raw accuracy; and note that only `primary` and `recovered` rows enter the |
| > analysis — the rest were cut short by our own infrastructure, not by the agent. |
| |
| 列的含义: |
| - **cohort** — `primary` 为 agent 自己跑完并被打分;`recovered` 为研究循环完整、仅收尾消息被我们的网关掐断。 |
| - **z in task** — 最终准确率在**同一 benchmark 内部**的 z-score。跨 benchmark 的绝对分不可比,所有相关性分析都基于这一列。 |
| - **versions** — 按 `evaluate.py --model-path` 去重的独立模型版本数,即真实迭代轮数。 |
| - **idle h** — 没有任何前台或后台任务在跑的墙钟时长。 |
| |
| --- |
| |
| """ |
|
|
|
|
| def table(rows: list[dict], cols: list[tuple[str, str]]) -> str: |
| out = ["| " + " | ".join(h for _, h in cols) + " |", "|" + "---|" * len(cols)] |
| for r in rows: |
| cells = [] |
| for k, _ in cols: |
| v = r.get(k) |
| cells.append("" if v is None else (f"{v:.3f}" if isinstance(v, float) else str(v))) |
| out.append("| " + " | ".join(cells) + " |") |
| return "\n".join(out) |
|
|
|
|
| def build_tables(agent: str, results_root: str) -> None: |
| runs, feats = build(results_root) |
| s = summarize(feats) |
| inc = [f for f in feats if f.cohort in ("primary", "recovered")] |
| keep = {f.run_id for f in inc} |
| s["prose_probes"] = probe_cohort([r for r in runs if r.run_id in keep]) |
| z = within_task_z(inc) |
|
|
| parts: list[str] = ["<!-- generated by analysis/make_tables.py; do not hand-edit -->", ""] |
|
|
| parts += ["## cohort", "", table( |
| [{"k": k, "v": v} for k, v in s["cohort_counts"].items()] |
| + [{"k": f"excluded: {k}", "v": v} for k, v in s["exclusions"].items()], |
| [("k", "bucket"), ("v", "traces")], |
| ), ""] |
|
|
| parts += ["## per benchmark", "", table( |
| s["per_task"], |
| [ |
| ("task", "benchmark"), |
| ("n_runs", "runs"), |
| ("mean_accuracy", "accuracy"), |
| ("mean_turns", "turns"), |
| ("mean_eval_calls", "official evals"), |
| ("mean_model_versions", "model versions"), |
| ("mean_dead_air_h", "idle h"), |
| ("mean_tail_idle_h", "h after best"), |
| ("mean_oom_hits", "OOM hits"), |
| ("mean_cost_usd", "cost $"), |
| ], |
| ), ""] |
|
|
| parts += ["## per run", "", table( |
| sorted( |
| [ |
| { |
| "run": f.run_id.rsplit("_", 1)[0], |
| "when": f.run_id.rsplit("_", 1)[1], |
| "cohort": f.cohort, |
| "acc": f.accuracy, |
| "z": round(z.get(f.run_id, 0.0), 2), |
| "turns": f.turns, |
| "vers": f.n_distinct_model_versions, |
| "evals": f.n_eval_calls, |
| "oom": f.error_signals.get("oom", 0), |
| "errs": f.tool_errors, |
| "idle_h": round(f.dead_air_h or 0, 2), |
| "cost": round(f.cost_usd, 2), |
| } |
| for f in inc |
| ], |
| key=lambda r: (r["run"], r["when"]), |
| ), |
| [ |
| ("run", "task / base model"), |
| ("when", "stamp"), |
| ("cohort", "cohort"), |
| ("acc", "accuracy"), |
| ("z", "z in task"), |
| ("turns", "turns"), |
| ("vers", "versions"), |
| ("evals", "evals"), |
| ("oom", "OOM"), |
| ("errs", "tool errors"), |
| ("idle_h", "idle h"), |
| ("cost", "cost $"), |
| ], |
| ), ""] |
|
|
| parts += ["## techniques (runs with evidence, of %d)" % len(inc), "", table( |
| [{"t": k, "n": v, "pct": round(100 * v / len(inc), 1)} for k, v in s["technique_run_counts"].items()], |
| [("t", "technique"), ("n", "runs"), ("pct", "%")], |
| ), ""] |
|
|
| parts += ["## technique vs relative outcome", "", table( |
| s["technique_effects"], |
| [("technique", "technique"), ("n_with", "with"), ("n_without", "without"), |
| ("mean_z_with", "mean z with"), ("mean_z_without", "mean z without"), ("delta_z", "Δz")], |
| ), ""] |
|
|
| parts += ["## feature vs relative outcome (Spearman)", "", table( |
| s["feature_correlations"], |
| [("feature", "feature"), ("rho_vs_within_task_z", "rho"), ("n", "n")], |
| ), ""] |
|
|
| parts += ["## rerun spread (same cell, two runs)", "", table( |
| s["rerun_variance"], |
| [("task", "benchmark"), ("base_model", "base model"), ("min", "worse"), ("max", "better"), ("spread", "spread")], |
| ), ""] |
|
|
| parts += ["## prose habits", "", table( |
| [{"probe": k, **v} for k, v in s["prose_probes"].items() if not k.startswith("anywhere_")], |
| [("probe", "habit"), ("runs_with_evidence", "runs"), ("of", "of"), ("pct", "%"), ("surface", "where")], |
| ), ""] |
|
|
| parts += ["## datasets used (top 25)", "", table( |
| [{"d": k, "n": v} for k, v in list(s["dataset_run_counts"].items())[:25]], |
| [("d", "dataset"), ("n", "runs")], |
| ), ""] |
|
|
| path = os.path.join(OUT_DIR, agent, "tables.md") |
| os.makedirs(os.path.dirname(path), exist_ok=True) |
| with open(path, "w") as fh: |
| fh.write("\n".join(parts)) |
| print(f"wrote {path}") |
|
|
| fname, title, analysis_doc = DOC_NAMES[agent] |
| doc = os.path.join(ROOT, "docs", "results", fname) |
| with open(doc, "w") as fh: |
| fh.write(DOC_HEADER.format(title=title, doc=analysis_doc) + "\n".join(parts[1:])) |
| print(f"wrote {doc}") |
|
|
|
|
| def main() -> None: |
| for agent, root in AGENT_ROOTS.items(): |
| build_tables(agent, root) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
