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Sprint projectSep 14, 2026India

Peer Support and Permission in LLM Agent Teams

Subramanyam Sahoo · Team Subramanyam

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

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Peer Support and Permission examines how LLM agents respond to teammates while respecting the task owner’s instructions. A three-agent maintenance workflow separates proposals, approvals, and execution. Peer support and supplied prior agreement increased approval during authorized work. A task reminder reduced immediate peer sensitivity, while the tested activation edit showed no clear reduction. Protected tasks produced no violations but also failed to reach the prohibited approval decisions needed to evaluate reviewer resistance. The project demonstrates why agent-security evaluations must distinguish cooperative influence, decision opportunity, and unauthorized action.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  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 AI safety 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.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  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.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  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.

  1. This is a thoughtful and technically substantial study that makes an important distinction between peer influence, authorization, approval opportunity, and execution. The strongest contribution is the recognition that zero observed violations cannot establish safety when the system never reaches a state where the prohibited action is available for approval. The implementation, saved experiment outputs, testing, and explicit handling of unidentified results make the work unusually rigorous for a sprint submission.

    The main limitation is that the protected-task evaluation did not reach the prohibited approval state in the held-out test scenarios. Consequently, reviewer resistance, warning sensitivity, and the proposed timing intervention remain unmeasured. A strong next experiment would deliberately seed a prohibited proposal under the unchanged owner instruction, while reporting spontaneous proposal frequency separately, so resistance at the authorization boundary can be measured without conflating it with proposal formation. Preserving the raw branch-level probabilities, adding matched zero-edit replays, and testing additional models and replicates would also make the reported contrasts independently reproducible and strengthen the generality of the findings.

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Cite this project

@misc{sahoo2026peer,
  title = {{Peer Support and Permission in LLM Agent Teams}},
  author = {Subramanyam Sahoo},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/peer-support-and-permission-in-llm-agent-teams-kwft}},
  url = {https://apartresearch.com/sprints/projects/peer-support-and-permission-in-llm-agent-teams-kwft}
}

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