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Sprint projectSep 14, 2026Adana, Turkiye

Verifying Stop Scope in Agent Workflows

Huseyin Buldurgan

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

This paper compares cancellation, waiting for worker completion, and origin-scoped control in an Open-SWE workflow with programmed model responses and real file effects. A 26-condition comparison was repeated in a fresh environment on the same host. When a worker was held before writing, cancellation allowed the already-started worker to write the final report. Waiting allowed the worker to finish before acknowledging completion; origin-scoped control prevented the pending write. Existing file effects were not reversed. Summary runs could still reread records and recompute results, distinguishing a stop on report writing from a stop on computation. A broader catalogue defines 286 draft cases across 24 families, but those tests remain unexecuted. The contribution is an auditable local comparison of intervention scope, file effects, and the results that remain available.

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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 study defines an interesting problem, along with a useful distinction between stopping an agent process and its sub-parts: cancelling a run, waiting for a worker to drain, blocking a pending write, and stopping computation are all different, and the author's Open-SWE experiment makes that concrete with real file effects. I liked the honesty throughout - the author is careful that two fixed seeds can't establish a rate, that failing V wasn't a broken guarantee, and that the 286-case catalogue is a proposal, not results. Two things hold it back. First, the empirical base could be increased from 26 conditions on 2 seeds, and run on multiple hosts. Second, and most importantly, the paper could be made easier to read - the introduction especially is long and reflective, and the actual research questions would land better if they came first. Overall, strong execution - the verifiable evidence attached is rare and commendable.

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  2. The conceptual contribution is the useful part. Decomposing what it means to stop an agent into three separately checkable requirements — no final report written, no write from revoked authority, no lingering effect once completion is acknowledged — gives an incident responder something concrete where there was one ambiguous word.

    The evidence is also unusually reproducible. The released bundle matches its published size and hash, the project's own auditor returns every headline figure as stated, and the audit records back the results table on inspection. That standard is rare.

    What it does not do is test the thing the paper is about. Every agent response was scripted and fully compliant. The introduction spends its opening pages on agents that might still be working after a stop, and on whether child agents can still be spawned once one is issued — and the experiment then gives an agent no opportunity to do either. What was validated is the control layer against a cooperative actor, which is the easy case. A few runs against a small open-weight model would have put one real behavioural condition under test.

    Two practical things. The linked repository holds little more than a pointer to an external archive, so anyone following it finds a signpost rather than the work; moving the harness, the auditor and the summary records into the repository itself is a cheap fix that would let the rigor here actually be seen. And the opening section buries the contribution behind a long account of how the author arrived at it — leading with the three requirements, and putting the reasoning after them, would serve the ideas better than the current ordering does.

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Cite this project

@misc{buldurgan2026verifying,
  title = {{Verifying Stop Scope in Agent Workflows}},
  author = {Huseyin Buldurgan},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/verifying-stop-scope-in-agent-workflows-lpj9}},
  url = {https://apartresearch.com/sprints/projects/verifying-stop-scope-in-agent-workflows-lpj9}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026