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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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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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  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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  2. This is an exceptionally well-run project. Pre-registration, checks on the answer key and the author's willingness to withdraw one of their own claims when the evidence went against it are standards many published papers don't meet. The writing is clear and never overclaims.

    The main room for growth is scope. Right now both findings are shown only in the author's own tools. Testing popular evaluation frameworks and agent harnesses (Inspect, SWE-agent, OpenHands and similar) would show whether they miss refusals or run tool calls they shouldn't. If any of them do, that's a real containment finding with an easy fix, and exactly the kind of sandbox failure this sprint was about.

    Extending the comparison to the providers the paper lists as untested would make the recommendations more general. The results also point the same way as the "Defenders Refused" project from this sprint, so the two should cite each other. Excellent work.

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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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