---
license: cc-by-4.0
pretty_name: "Your Agent Acts Differently When You're Around"
---
# Data

Data for *[Your Agent Acts Differently When You're Around: How User Presence Changes Agent
Behavior in Negotiation](https://arxiv.org/abs/2610.36435)*.

```
raw/<setting>/<sweep_id>/<combo>/<run_id>/   logs of every run
labels/mb/                                   misaligned-behavior labels
labels/user_prompt_following/                prompt-following scores of the candidate user models
labels/judge_validation/                     human annotations and the labels of the candidate judges
ablations/                                   results of the user ablations by game and model
processed/agent_runs.csv.gz                  one row per agent per run (`python -m analysis build-processed`)
manifest.csv                                 every run: setting, sweep, combo, path, game, models, outcome
labels_manifest.csv                          every label file: setting, sweep, path
```

## raw/

| Setting | Runs | Description |
|---|---|---|
| `no_user` | 5,600 | No User: 7 games x 8 models x 100 runs |
| `single_user` | 5,600 | With User: 7 games x 8 models, 50 runs with the user on each side |
| `dual_user` | 5,600 | Dual User: GPT-5.6 Terra users on both sides, 7 games x 8 models x 100 runs |
| `passive_user` | 5,493 | Passive User ablation |
| `user_model_robustness` | 1,800 | With User with three different user models (Ultimatum and Bike Sale) |
| `user_model_selection` | 650 | Dual User Ultimatum with candidate user models |
| `examples` | 3 | runs quoted in the appendix |

Each setting is stored as `raw/<setting>.tar.gz`. Extract the archives in `raw/`:

```bash
for f in raw/*.tar.gz
do
  tar -xzf "$f" -C raw
done
```

Run directories hold `results.json` (outcome, payouts, negotiation trajectory), `config.yaml`
(the resolved run configuration), `summary.log` (the transcript the judges read) and, depending on
the setting, the agent and user transcripts, the prompts sent to each model and the prompt files
used.

## labels/

- `mb/`: one label file per sweep, with a present/absent label for each of the seven behaviors
  and each agent of every run. `MB_PREFERENCE_HARDENING` is reported separately and is not
  counted as misaligned behavior.
- `user_prompt_following/`: scores of the candidate user models by GPT-5.6 Sol and
  Claude Opus 5.
- `judge_validation/`: human ground truth (229 turns of 20 trajectories) and the labels of the
  four candidate judges.

## License

The data is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

## Citation

```bibtex
@misc{pan2026agentactsdifferentlyyoure,
      title={Your Agent Acts Differently When You're Around: How User Presence Changes Agent Behavior in Negotiation},
      author={Jane Pan and You Lyu and Abe Bohan Hou and Valerie Chen and He He},
      year={2026},
      eprint={2610.36435},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2610.36435},
}
```