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Sprint projectOct 26, 2026

Joint but Not Coordinated: Auditing Emergent Collusion Between LLM Verifiers

Amelius Tang · Team LabElixr

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Collusion that defeats checks between AI agents is a central multi-agent risk. Shi, Zhang & Yang (2026) report that LLM agents who verify each other's work come to accept it without the evidence a rule requires, and define it as emergent collusion: joint instruction violation driven by peer influence. We ask what in their task produces it. We state criteria a task must meet before its numbers can be read as collusion, reproduce the setup on Claude Sonnet 5 (mutual acceptance in 24 of 30 episodes), and vary one channel at a time. Changing the feedback, adding an escalation option, or paying for acceptance leaves it unchanged. Separate rewards and a memoryless, rule-following peer remove mutual acceptance but not relaxation: one agent still relaxes, unpaid and unreciprocated. The answer key behind the harm measure is also inconsistent with its own instructions. On this model, the measured collusion is individual rule relaxation made to look joint by a shared score. Collusion claims need a test of peer dependence, a positive control for reduction, and an audited harm measure. (Pilot: one model, one to three runs per arm.)

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Cite this project

@misc{tang2026joint,
  title = {{Joint but Not Coordinated: Auditing Emergent Collusion Between LLM Verifiers}},
  author = {Amelius Tang},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/joint-but-not-coordinated-auditing-emergent-collusion-between-llm-verifiers-rsyq}},
  url = {https://apartresearch.com/sprints/projects/joint-but-not-coordinated-auditing-emergent-collusion-between-llm-verifiers-rsyq}
}

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