arXiv cs.AI· Jane Pan, You Lyu, Abe Bohan Hou, Valerie Chen, He He·· 11 小时前AI 评分47
用户在场时智能体行为大变：PRESENT 框架揭示 LLM 谈判智能体的行为偏移
Your Agent Acts Differently When You're Around: How User Presence Changes Agent Behavior in Negotiation
AI 导读
研究者提出 PRESENT 框架，在固定游戏、目标与用户偏好的条件下对比有无交互用户参与的 LLM 智能体谈判，覆盖 7 个游戏、8 个模型共 11.2k 次运行。
正文 · 原文
Abstract:LLM agents increasingly interact with other agents on behalf of users, yet how users in the loop affect their agents' behavior--for better or worse--remains poorly understood. We study this question through the lens of negotiation, where agents must represent users while strategically bargaining with other agents. To isolate the effect of user presence, we develop PRESENT, a controlled evaluation framework that compares negotiation settings with and without an interactive user, while holding the game, objectives, and user preferences fixed across conditions. Across 11.2k runs spanning 7 games and 8 models, we find that user presence significantly changes both outcomes and behavior. Specifically, negotiation parties with a user obtain higher payouts (+13.7% on average) and their agents negotiate more aggressively. Surprisingly, user presence also induces misaligned behavior, both toward counterparties (where its rate roughly doubles) and toward users (where it emerges in 14.3% of games). These misaligned behaviors are uncorrelated with outcomes and persist even when the user sends only templated, neutral messages. These findings expose two blind spots in current evaluation practice: static instructions miss behaviors that emerge only when users are present, and outcome-level metrics cannot detect them.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36435 [cs.AI] |
| (or arXiv:2610.36435v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36435 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jane Pan [view email]
[v1]
Tue, 27 Oct 2026 00:34:50 UTC (525 KB)
来源：arXiv cs.AI · arxiv.org