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Sprint projectSep 13, 2026bristol

Refusals a Text-Only Scorer Cannot See: Structured Refusal Signals on a Synthetic Incident-Response Battery

Warren Smith · Team Omega

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

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Report: Refusals a Text-Only Scorer Cannot See: Structured Refusal Signals on a Synthetic Incident-Response Battery

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Tests whether AI refusals can disrupt legitimate incident-response work and go undetected by evaluation systems. Across a synthetic forensic battery, one deployment refused half the tested prompts, while a text-only scorer failed to identify every refusal because the signal existed only in structured APImetadata

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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 strong, practically useful study of a flaw in AI testing setups that is easy to miss. The key finding isn't just that one tested AI refused to help with incident response tasks. It's that those refusals were invisible to the scoring tool, which only read the AI's written reply.

    In the main run, all 16 refusals came back looking like a normal, successful response, but with no text in it. The only sign of a refusal was a separate behind the scenes label saying the AI had declined. Because the scorer only looked at the text, it marked every one of them as "unclear." A rule that read that hidden label correctly identified all 16. This shows how a testing setup could easily mistake a refusal for a glitch, a missing answer, or a failure of ability.

    The study also found a useful safety issue with tools. In two cases, the AI had signaled a refusal but its reply still included a request to run a tool, and the testing software ran the tool before checking whether the AI had refused. This leads to a simple, actionable recommendation: keep the behind-the-scenes stop signals, and check them before running any tools or judging a reply by its text alone.

    The researchers are careful about the study's limits and open about them. The main next step is to repeat it with other AI providers, accounts, dates, and refusal detection methods, run more trials, and remove the confusing effect of the tool setup.

    Overall, this is a well-done study with a lesson that can be used right away by anyone building AI tests or systems that let AI take actions.

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Cite this project

@misc{smith2026refusals,
  title = {{Refusals a Text-Only Scorer Cannot See: Structured Refusal Signals on a Synthetic Incident-Response Battery}},
  author = {Warren Smith},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/refusals-a-textonly-scorer-cannot-see-structured-refusal-signals-on-a-synthetic-incidentresponse-battery-c4bt}},
  url = {https://apartresearch.com/sprints/projects/refusals-a-textonly-scorer-cannot-see-structured-refusal-signals-on-a-synthetic-incidentresponse-battery-c4bt}
}

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