| # OneDayAgent Trajectory Data |
|
|
| Execution trajectories and LLM-as-judge scores for all experiments reported in |
| *OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents* (Zheng et al., 2026), |
| evaluated on the AgentIF-OneDay benchmark (104 tasks, 767 instance-level rubric points). |
|
|
| This bundle contains the raw evidence chain for every reported run: the full |
| agent trajectory, the final deliverable artifacts, and the per-criterion judge |
| scores. Every headline number in the paper is directly reproducible from the |
| files here, with no external dependencies. |
|
|
| --- |
|
|
| ## 1. Runs included |
|
|
| Nine runs, each covering the full 104-task AgentIF-OneDay suite. Six main |
| backends (Section 3.2 / Table 3) plus one additional baseline and three |
| ablation variants (Section 3.3 / Table 4). |
|
|
| | Run directory | Paper role | Backend LLM | Paper overall score | |
| |------------------------------------------------|-------------------|----------------------------|----------------------| |
| | `onedayagent_glm52_20260623_132100_8210` | OneDayAgent (main)| GLM-5.2 | **0.821** | |
| | `onedayagent_gemini31propreview_20260513_*` | backend variant | Gemini-3.1-Pro-Preview | 0.743 | |
| | `onedayagent_qwen35_397b_20260610_*` | backend variant | Qwen3.5-397B-A17B | 0.708 | |
| | `onedayagent_qwen35_9b_20260507_*` | backend variant | Qwen3.5-9B | 0.624 | |
| | `onedayagent_qwen36_27b_20250618_*` | backend variant | Qwen3.6-27B | 0.613 | |
| | `codex_gpt55_20260622_174300_6643` | baseline | Codex (GPT-5.5 medium) | 0.664 | |
| | `ablation_study/ablation_glm52_react_direct_*` | DIRECT | GLM-5.2 | 0.771 | |
| | `ablation_study/ablation_glm52_decompose_only_*`| DECOMP | GLM-5.2 | 0.804 | |
| | `ablation_study/ablation_glm52_verify_only_*` | VERIFY | GLM-5.2 | 0.804 | |
|
|
| The `FULL` ablation variant is not duplicated under `ablation_study/`; it is |
| identical to the main `onedayagent_glm52_*` run (both modules enabled). |
|
|
| --- |
|
|
| ## 2. Uniform per-run layout |
|
|
| Every run has the same six components. |
|
|
| ``` |
| <run>/ |
| ├── auto_score_<ts>.jsonl # 767 lines — per-rubric judge scores |
| ├── auto_score_<ts>.txt # human-readable aggregate of the above |
| ├── run_<ts>.log # runtime log |
| ├── env_snapshot.txt # runtime environment config |
| ├── <Backend>_<ts>/ # backend subdir, contains only: |
| │ └── rollout1.jsonl # 104 lines — one full trajectory per task |
| └── taskif_<id>_<ts>/ # 104 dirs — final deliverable artifacts per task |
| └── ... # whatever the agent produced (xlsx/png/md/pptx/...) |
| ``` |
|
|
| These six items form the complete evidence chain |
| (task definition → agent execution → final artifact → judge score) for every run. |
|
|
| --- |
|
|
| ## 3. `rollout1.jsonl` — the core trajectory file |
|
|
| One JSON object per line, 104 lines per run. Same schema for every backend, |
| including the Codex baseline (only `trajectory.ext_info.agent` differs, |
| `ReactAgent` vs `CodexAgent`). |
|
|
| ### Top-level fields (per line) |
|
|
| | Field | Type | Content | |
| |---|---|---| |
| | `question_id` | str | e.g. `taskif_111` | |
| | `title`, `description` | str | task statement | |
| | `attachment_filenames` | list[str] | user-provided input files | |
| | `score_criteria` | list[obj] | all rubric points: `{content, score}` (the 767 total) | |
| | `reference_answer_attachment_filenames` | list[str] | reference deliverables | |
| | `task_tag` | str | interaction pattern: `Open Workflow Execution` / `Latent Instruction Inference` / `Iterative Refinement` | |
| | `domain_tag` | str | `Work` / `Life` / `Study` | |
| | `rubrics_tag` | str | `Execution` / `Content` / `Form` | |
| | `time` | str | time budget: `<1h` / `1-4h` / `4-8h` / `8-12h` / `12-24h` / `24+h` | |
| | `question` | str | full prompt sent to the agent | |
| | `prediction` | str | agent's final textual answer | |
| | `time_cost` | float | wall-clock latency in seconds (Table 3 Latency column) | |
| | `result_files` | list[str] | final deliverable filenames (match `taskif_<id>_*` contents) | |
| | `task_ts` | str | per-task start timestamp `YYYYMMDD_HHMMSS` | |
| | `trajectory` | obj | full conversation (see below) | |
|
|
| ### `trajectory` sub-object |
|
|
| ``` |
| trajectory: |
| guid : str — trajectory id |
| system_message : {token_cost, role, content, tool_specs} — system prompt + tool schemas |
| conversations : list[stage] — ordered execution stages (see below) |
| ext_info : {type, model, agent, task_description, task_seed} |
| create_time : str — ISO timestamp |
| ``` |
|
|
| ### `conversations` — execution stages |
|
|
| Each run is split into ordered stages. A typical full OneDayAgent task has: |
|
|
| | idx | stage | `questions` | `solutions` | `answer` | |
| |-----|------------|------------------------|--------------------|-----------------------| |
| | 0 | planning | task + planner output | — | subtask JSON list | |
| | 1..n| subtask | subtask prompt | ReAct turns (reason/act/observe) | subtask summary | |
| | n+1 | synthesis | synthesis prompt | — | candidate deliverable | |
| | n+2 | verify | verification prompt | — | `{completed, reason, missing_items, suggestions}` | |
| | n+3 | repair? | (only when verify fails) repair feedback + ReAct turns | repaired deliverable | |
|
|
| The DIRECT ablation has a single subtask stage and no verify/repair. The |
| DECOMP variant has subtask decomposition but no verify/repair. The VERIFY |
| variant has verify/repair but no decomposition. |
|
|
| --- |
|
|
| ## 4. `auto_score_*.jsonl` / `.txt` — judge scores |
| |
| ### `auto_score_*.jsonl` — 767 lines (one per rubric criterion, summed across 104 tasks) |
|
|
| ```json |
| { |
| "question_id": "taskif_111", |
| "agent_name": "react", |
| "method": "gemini-3.1-pro-preview", // the judge model |
| "criterion_content": "The returned file accurately names the subtable \"March\"...", |
| "criterion_score": 1, // 0 or 1 |
| "satisfied": true, |
| "reasoning": "The answer successfully created a new worksheet named 'March'..." |
| } |
| ``` |
|
|
| `method` is the LLM-as-judge (Gemini-3.1-Pro-Preview, temperature 0.1, |
| 65 536 max tokens; see Table 2). `agent_name` is always the literal `"react"` |
| regardless of the actual backend — identify runs by directory name, not this |
| field. |
|
|
| ### `auto_score_*.txt` — pre-aggregated report |
| |
| Contains the headline number and all Table 3 / Table 4 breakdowns (by task |
| type, domain, rubric dimension, time budget, with/without attachments). The |
| `Average score` line is exactly the paper's normalized overall score ×100. |
| |
