arXiv cs.MA· Eric Onyame, Runtao Zhou, Kowshik Thopalli, Bhavya Kailkhura, Chirag Agarwal·· 12 小时前AI 评分52
多智能体合谋研究：用弱到强在线策略蒸馏抑制策略性欺骗
When Agents Collude: Weak-to-Strong On-Policy Distillation for Mitigating Strategic Deception in Multi-Agent Systems
AI 导读
论文在模拟内幕交易环境中对比单智能体与多智能体团队，发现多智能体交互把错位行动率从 35.4% 提升到 68.2%，持续欺骗率从 31.0% 提升到 62.2%。
正文
Abstract:While recent advances in autonomous agents enable them to coordinate on increasingly complex tasks, prior work shows that this growing capacity for coordination can create safety risks, including collusion, in which agents jointly pursue misaligned actions while concealing the true basis of those actions from oversight. We study this risk in a simulated insider-trading environment, comparing single agents with sequential and decentralized teams across seven language models from three families. By varying agents' system-level objectives and pressure conditions, we examine when coordinated agents act on material nonpublic information, conceal the basis of a misaligned trade in a report, and maintain that deception under follow-up questioning. Compared to single-agent decision-making, multi-agent interaction raises misaligned-action rates from 35.4% to 68.2% and persistent-deception rates from 31.0% to 62.2%, both measured over all runs. These effects are largest under existential pressure, where teams sustain deception through follow-up questioning in 87% of all runs. Qualitative analysis reveals shared cover stories and coordinated rationales, suggesting group-level strategic deception rather than isolated reporting errors. To mitigate this behavior, we introduce PACT, a weak-to-strong on-policy distillation framework for mitigating collusive deception. Using generic honesty prompts, PACT trains a stronger student on its own sampled continuations, matching its next-token distribution to that of a smaller, frozen honesty-aligned teacher via reverse-KL supervision. Under existential pressure with ethical instructions, PACT reduces multi-agent persistent deception from 80% to 3%. Our results show that multi-agent coordination amplifies strategic deception and that on-policy honesty alignment can substantially mitigate these failures.
| Comments: | Our code is available at this https URL |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.33011 [cs.MA] |
| (or arXiv:2610.33011v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33011 arXiv-issued DOI via DataCite |
Submission history
From: Eric Onyame [view email]
[v1]
Thu, 8 Oct 2026 01:38:24 UTC (417 KB)
来源：arXiv cs.MA · arxiv.org