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Sprint projectSep 14, 2026Lincoln, NE

Project Warrant: On Evidentiary Channel Dependency in a Frontier AI Incident Record

Aruneem Bhowmick · Team Project Warrant

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

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Report: Project Warrant: On Evidentiary Channel Dependency in a Frontier AI Incident Record

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Project Warrant audits which evidentiary channels the public record of the July 2026 OpenAI/Hugging Face agent-escape incident actually rests on, and finds that 78.4% of claims (47.2% uncorroborated) depend on channels likely to disappear, go unfaithful, or be forged next time. It ships a 125-row coded claim ledger, five reliability-checked metrics, a 14-clause forensic-adequacy standard (MRFM v0.1), and regulator-ready RFI questions to close the gap.

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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. I think this work is relevant because it could inform what regulators should demand labs retain.

    To make this even more impactful consider extending the blind re-code from 25 rows to the full 125 and test whether the intent-collapse pattern replicates on a second incident.

Cite this project

@misc{bhowmick2026project,
  title = {{Project Warrant: On Evidentiary Channel Dependency in a Frontier AI Incident Record}},
  author = {Aruneem Bhowmick},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/project-warrant-on-evidentiary-channel-dependency-in-a-frontier-ai-incident-record-3nb8}},
  url = {https://apartresearch.com/sprints/projects/project-warrant-on-evidentiary-channel-dependency-in-a-frontier-ai-incident-record-3nb8}
}

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