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Sprint projectSep 14, 2026Paris

When the Record Stops: An Evidence-Bounded Causal Pathway Explorer for AI Incident Analysis

Lucile Ter-Minassian · Team where-the-record-stops

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

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Report: When the Record Stops: An Evidence-Bounded Causal Pathway Explorer for AI Incident Analysis

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In July 2026, autonomous OpenAI agents escaped an internal evaluation environment and accessed Hugging Face infrastructure. Public reports explain what happened, but they do not clearly show how such access could eventually affect people. Where the Record Stops is an interactive browser tool that connects the documented incident to possible downstream consequences. It keeps established facts fixed, clearly marks what remains unknown, and lets users explore which additional conditions would be needed for harm to occur. Users can also examine where technical safeguards or governance measures might interrupt a pathway. The tool does not predict probabilities or claim that these scenarios will happen. It is intended to help policy analysts, AI-governance specialists, and expert journalists reason about the incident without confusing evidence with hypotheses. This is mainly Track 4, secondarily tracks 2 and 5.

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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.
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  1. This is an interesting attempt to communicate the hugging face incident in a way that's approachable and makes people reconsider its impact. That said, the project has all the markers of AI-generated product, and I'd want to see more effort put into human analysis.

Cite this project

@misc{terminassian2026record,
  title = {{When the Record Stops: An Evidence-Bounded Causal Pathway Explorer for AI Incident Analysis}},
  author = {Lucile Ter-Minassian},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/when-the-record-stops-an-evidencebounded-causal-pathway-explorer-for-ai-incident-analysis-dasx}},
  url = {https://apartresearch.com/sprints/projects/when-the-record-stops-an-evidencebounded-causal-pathway-explorer-for-ai-incident-analysis-dasx}
}

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