| --- |
| 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}, |
| } |
| ``` |
|
|
