# 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. ``` / ├── auto_score_.jsonl # 767 lines — per-rubric judge scores ├── auto_score_.txt # human-readable aggregate of the above ├── run_.log # runtime log ├── env_snapshot.txt # runtime environment config ├── _/ # backend subdir, contains only: │ └── rollout1.jsonl # 104 lines — one full trajectory per task └── taskif__/ # 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__*` 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.