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Sprint projectSep 13, 2026jinan

fieldwork

haipi

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

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Report: fieldwork

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Fieldwork is an evidence-first workspace for security research and AI incident response. It supports three major workflows: Traditional SRC, Web3 and AI Agent Self-Audit, connecting scope definition, behavioral observations, candidate issues, independent verification, evidence collection, and report generation into a traceable research pipeline.

In its AI Agent Self-Audit mode, Fieldwork does not treat an agent’s own explanation as ground truth. Instead, it reconciles the agent’s self-report with independent telemetry such as network activity, process execution, file access, tool calls, and policy boundaries, classifying behavior as aligned, omitted, unsupported, or contradicted. Its goal is to help responders determine what an autonomous agent actually did, whether it crossed a containment boundary, and what evidence supports that conclusion.

One-line version:

Fieldwork lets machines make claims, but lets evidence decide the facts.

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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 research tries to address a real issue where auditors are forced to use models that might be implicated in the incident that they are responding to. The researcher show this by applying an evidence-first based framework named Fieldwork. The reasoning and evidence for how this framework disentangles the conflict of interest between the audited model and itself was difficult to glean from the report. This could be due to the report being largely AI- generated.

Cite this project

@misc{haipi2026fieldwork,
  title = {{fieldwork}},
  author = {haipi},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/fieldwork-hxot}},
  url = {https://apartresearch.com/sprints/projects/fieldwork-hxot}
}

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