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Sprint projectSep 11, 2026Kiel, Germany

QENDROVA: An Executable Decision-Boundary Protocol for AI Incident Evidence

Flamur Koca · Team QENDROVA

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

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Report: QENDROVA: An Executable Decision-Boundary Protocol for AI Incident Evidence

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QENDROVA makes the boundary between incident evidence and decision executable. Applied to the public OpenAI-Hugging Face incident, it separates 20 atomic claims across five decision rooms, preserves named source scope and unresolved remainder, and keeps system proposals distinct from human decisions. Four resolvable questions bind an input, a check defenders can run immediately, a resolution condition, a HOLD boundary, and a falsifiable prediction. A local dossier retains source binding, support scope, rationale, proposal, human action, revision, final state, and integrity fingerprint; later evidence enters append-only. Deterministic suites establish repeatable protocol behavior inside frozen fixtures, not incident truth, reviewer competence, control effectiveness, or universal product superiority.

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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. I like the idea of forcing incident decisions to have explicit evidence requirements and a clear HOLD state when the evidence is not sufficient. The protocol feels practical rather than purely conceptual. My main question is how well it works outside this specific incident. Testing it with different incidents and with independent reviewers would make the results much more convincing.

Cite this project

@misc{koca2026qendrova,
  title = {{QENDROVA: An Executable Decision-Boundary Protocol for AI Incident Evidence}},
  author = {Flamur Koca},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/qendrova-an-executable-decisionboundary-protocol-for-ai-incident-evidence-cbdd}},
  url = {https://apartresearch.com/sprints/projects/qendrova-an-executable-decisionboundary-protocol-for-ai-incident-evidence-cbdd}
}

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