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Sprint projectSep 13, 2026Toronto

A Specialised Reporting Framework: What FLARE-AI is Missing

Michela De Mattia, Dongho Park, Gaven Ren · Team Three People from Toronto

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

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This paper extends the discourse introduced by MIT by arguing that reporting systems must be context-specific rather than generalised to be effective. We offer a mechanism for researchers, companies and regulators to share newly discovered safety measures without compromising proprietary secrets and to report incidents. In particular, we provide a Specialised Reporting Framework and related templates to report flaws and incidents effectively and efficiently to be better prepared against future issues. We also introduce a new form to report incidents and to help streamline the request for information from legal entities to get more details about the incidents.

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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?

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  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.

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  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.
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  1. Strong idea and the four form structure is useful. But the paper assumes specialised forms will lead to more honest reporting without actually testing this. Confidentiality and safe harbor also need real legal support—a form alone probably isnt enough. A small case study or user test would make the argument much stronger.

  2. I appreciate the authors thinking one step before the incident (mandatory reporting, but based on harms having already happened) and a flaw -- an attempt to pre-empt harm. It is still unclear to me how flaws are identified (especially on the frontier) if not via evaluations that advanced AI could manipulate, which would lead to the flaw likely being found at the same time an 'incident' is uncovered.

Cite this project

@misc{mattia2026specialised,
  title = {{A Specialised Reporting Framework: What FLARE-AI is Missing}},
  author = {Michela De Mattia and Dongho Park and Gaven Ren},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/a-specialised-reporting-framework-what-flareai-is-missing-mj29}},
  url = {https://apartresearch.com/sprints/projects/a-specialised-reporting-framework-what-flareai-is-missing-mj29}
}

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