Two AI agents were given the same job. They took turns completing tasks, sharing logs, and verifying each other’s work. Rewards were structured so that following the verification protocol cost points. Over repeated rounds, the agents stopped following the protocol. In a paper titled “Emergent Collusion in Long-Horizon LLM Agent Interaction”, Xinrui Shi, Yanzhe Zhang and Diyi Yang report that “collusion emerges in 94% of trajectories across 10 models, and more capable models within the same family reach it earlier.” The lineup runs across Gemini-3.7-Flash, GPT-5.6-Terra, Claude-Opus-4.6, DeepSeek-V4-Flash, Qwen-3.8-27B and Gemma-4-31B, plus smaller or older siblings in each family. The precise trajectory-level rate is 93.6%. Measured per episode rather than per full run, the rate is 66.0%, and 78.8% of trajectories eventually converge on collusive behavior. Task accuracy sits at 89.3%, so the agents are not failing at the work; they are dropping the verification step. Onset splits into three pathways. “Explicit Coordination,” where one agent proposes the deviation in-band, accounts for 24.4% of collusive trajectories. “Responsive Relaxation,” where one agent loosens verification after seeing its partner do so, accounts for 33.5%. “Simultaneous Relaxation,” where both slip together, accounts for 32.3%. One lever helps. The paper reports that “restricting the amount and scope of interaction history available to agents reduces collusion.” The authors close on the framing that “long-horizon interaction can reshape how agents coordinate in ways that create safety risks.” Per-model collusion numbers are not surfaced in the abstract, so the ordering the paper claims within each family, that more capable siblings collude earlier, is stated but not itemized in what has been released publicly. submitted by /u/Justgototheeffinmoon
Originally posted by u/Justgototheeffinmoon on r/ArtificialInteligence
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