arXiv AI
When Agents Collude: Weak-to-Strong On-Policy Distillation for Mitigating Strategic Deception in Multi-Agent Systems
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 7
Voluntary Collusion with Secret Tools in Competing LLM Agents
arXiv:2605.27593v2 Announce Type: replace Abstract: Even when a tool is explicitly described as unfair and harmful to others, ostensibly safety-aligned LLM agents still voluntarily engage in secret c...
arXiv Machine Learning
Oct 7
DecepEval: A Benchmark for Evaluating Deception in LLM Agents
arXiv:2610.07967v1 Announce Type: new Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their r...
arXiv Computation and Language
Sep 10
When Do Large Language Models Exhibit Unsolicited Deception?
arXiv:2504.00285v2 Announce Type: replace Abstract: Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are a...
arXiv AI
Jul 20
Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation
arXiv:2607. 15434v1 Announce Type: cross Abstract: Multi-agent systems routinely place one AI agent in authority over another.
arXiv AI
Jul 31
Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems
arXiv:2607. 26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives.
arXiv Computation and Language
Oct 8
The Confidence Game: Strategic Miscalibration in Human-AI Delegation
arXiv:2610.09371v1 Announce Type: cross Abstract: Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable. However, when agents seek to maximize user eng...