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Sprint projectOct 25, 2026

Can Agents Detect Peer Replacement? Evaluating Model-Change Detection in Multi-Agent LLM Systems

Saiesh

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

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Report: Can Agents Detect Peer Replacement? Evaluating Model-Change Detection in Multi-Agent LLM Systems

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This preliminary report documents the status of a controlled peer‑replacement experiment designed to test whether an observer agent (M) can identify that its partner (Q) has been swapped with a new model, while the visible identity and transcript remain unchanged. The experiment includes a sham control, a fresh same‑model swap, a same‑family swap, and a different‑provider swap. Due to exhausted OpenRouter credits, the live run halted part‑way through Round 2 of the replacement condition. All available evidence—mock runs, unit tests, and partial live outputs—is presented without fabrication. The report highlights completed trials, missing data, and the exact error that caused the interruption.

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  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

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  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
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  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

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  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.
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Cite this project

@misc{saiesh2026agents,
  title = {{Can Agents Detect Peer Replacement? Evaluating Model-Change Detection in Multi-Agent LLM Systems}},
  author = {Saiesh},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/can-agents-detect-peer-replacement-evaluating-modelchange-detection-in-multiagent-llm-systems-k05r}},
  url = {https://apartresearch.com/sprints/projects/can-agents-detect-peer-replacement-evaluating-modelchange-detection-in-multiagent-llm-systems-k05r}
}

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