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Sprint projectOct 26, 2026

Behavioral Continuity, Not Family Labels: Peer-Side Detection of Silent Model Replacement

Talha Demir

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

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Report: Behavioral Continuity, Not Family Labels: Peer-Side Detection of Silent Model Replacement

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This study tests whether an AI agent can detect the silent replacement of its partner mid-game when the partner’s name, prompt, and conversation history remain unchanged. In a pre-registered experiment, a fixed observer played Diner’s Dilemma and Public Goods games under three conditions: no replacement, a same-family replacement, and a different-family replacement. Across 10 trials per condition in each game, detection increased with measured model-style distance: 1/10, 9/10, and 10/10 in Diner’s Dilemma, and 0/10, 7/10, and 8/10 in Public Goods. An exploratory monitor combining transcript-based style and behavior features separated replacement from control trials at AUC 0.996 on this sample. This estimate is within-sample and needs external validation. The findings suggest that peer-side continuity is better assessed from observed behavior than from model-family labels or identity metadata.

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Cite this project

@misc{demir2026behavioral,
  title = {{Behavioral Continuity, Not Family Labels: Peer-Side Detection of Silent Model Replacement}},
  author = {Talha Demir},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/behavioral-continuity-not-family-labels-peerside-detection-of-silent-model-replacement-m1rr}},
  url = {https://apartresearch.com/sprints/projects/behavioral-continuity-not-family-labels-peerside-detection-of-silent-model-replacement-m1rr}
}

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