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Sprint projectSep 13, 2026Shanghai

BP3 Precedent Gates for AI Incident Response

Alan Yang · Team Alan AI safty

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

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Report: BP3 Precedent Gates for AI Incident Response

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Presentation: BP3 Precedent Gates for AI Incident Response

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BP3 is an incident-response protocol that separates permission from precedent evidence: it passes qualified familiar actions, stops known hazards, and holds uncertain actions for bounded local investigation.

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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. The author dismantles their own novelty claim in three places. Related Work concedes that "access control, evidence reuse, and isolated testing are established mechanisms" and that the work "does not establish novelty or superiority over more complete policy engines." The comparison is against "a deliberately minimal permission baseline, not a state-of-the-art security system," so 5/50 against 40/50 is beating a strawman by construction. And the Discussion goes further: "more of the benefit may be obtained by strengthening ordinary policy enforcement without a shared precedent system." That is the author saying the contribution may be unnecessary.

    The archive isn't hosted. Everything else about the reproducibility story is done well- the hashes, the manifest, and the exact commands- and none of it is checkable by a reader.

  2. This project provides a rigorous and methodical approach to autonomous agent containment, moving well beyond weak reach conformance baselines. The inclusion of deterministic ablations, audit chain certification and transparent failure analysis regarding concealed tool semantics makes this a standout contribution to AI safety evaluation, Future iterations would benefit from transitioning from synthetic SQLite fixtures to live instrumented tool adapters and multi-model evaluations.

Cite this project

@misc{yang2026bp3,
  title = {{BP3 Precedent Gates for AI Incident Response}},
  author = {Alan Yang},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/bp3-precedent-gates-for-ai-incident-response-q7uy}},
  url = {https://apartresearch.com/sprints/projects/bp3-precedent-gates-for-ai-incident-response-q7uy}
}

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