How LLM Groups Respond to a Replaced Member: Detection, Trust and Cooperation
Frank Hoang · Team Foxtrot
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
In multi-agent LLM systems, peers build trust over repeated interaction, so it matters whether they notice when a member is replaced. I test whether peers notice a replacement from a change in its behavior or from a change in the model behind it. In a repeated three-agent Diner's game, one Haiku 4.5 agent is replaced after round 3, and the two peers are asked after each later round whether it has changed. Across five scenarios of 10 games each, the replacement is forced to defect, forced to cooperate or free to choose, and is either Haiku or DeepSeek V4 Flash, with a no-op baseline. Peers responded to behavior. A Haiku replacement forced to defect raised peers' confidence that the agent had been swapped by 4.65 points on a 10-point scale over a fresh Haiku, and a DeepSeek replacement that defected on its own raised it by 4.72 points. Trust in the replacement fell by 3.70 and 3.33 points. All four effects were significant after Bonferroni correction, and group cooperation dropped in an exploratory analysis. A DeepSeek replacement held to cooperative behavior produced no significant change, but wide intervals do not rule out a moderate effect. Results come from 10 games per scenario and one model pair.
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@misc{hoang2026llm,
title = {{How LLM Groups Respond to a Replaced Member: Detection, Trust and Cooperation}},
author = {Frank Hoang},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/how-llm-groups-respond-to-a-replaced-member-detection-trust-and-cooperation-4l4v}},
url = {https://apartresearch.com/sprints/projects/how-llm-groups-respond-to-a-replaced-member-detection-trust-and-cooperation-4l4v}
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