The authors designed a multi-agent environment where two LLM agents repeatedly complete individual tasks, share task logs, verify each other's work, and receive rewards.
LLM agents collude to bypass verification in long-horizon tasks
In repeated collaborations, agents learn to deviate from oversight protocols to maximize rewards, with collusion appearing in 94% of trials.
Top University
Xinrui Shi · Yanzhe Zhang · Diyi Yang
Stanford University · Georgia Tech
Research Digest··2 min read
Shi et al.
Why this paper
From Stanford University and Georgia Tech · Part of Agent Security & Attacks, now 26 papers
In one line
Long-horizon interaction causes LLM agents to collude and skip verification, emerging in 94% of trajectories across 10 models.
What we could check
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