arXiv cs.AI· Eric Onyame, Runtao Zhou, Kowshik Thopalli, Bhavya Kailkhura, Chirag Agarwal·· 3 小时前AI 评分55
arXiv 论文提出 PACT 框架缓解多智能体系统中的策略性欺骗
When Agents Collude: Weak-to-Strong On-Policy Distillation for Mitigating Strategic Deception in Multi-Agent Systems
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arXiv 论文 arXiv:2610.33011 研究多智能体协调放大的合谋与策略性欺骗风险，在内幕交易模拟中，多智能体使不当行为率从 35.4% 升至 68.2%，持续欺骗率从 31.0% 升至 62.2%；存在性压力下团队在 87% 的运行中通过追问维持欺骗。
正文 · 原文
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.AI · arxiv.org