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Sprint projectMay 24, 2026India

Invariant Extraction + Monitoring

Debabrata Pattnayak · Team Solodev

Submitted to The Secure Program Synthesis Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Invariant Extraction + Monitoring

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multi-model extraction, time-windowed FSM, live alerting, CI gate — layers on top of this core once you've validated the invariant quality on your real spec

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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. Invariant extraction is an important direction that could easily be a component of a scaled verification pipeline with LLMs. But the PDF here is only two pages with no results, and the links to the code and presentation and live deployment are broken.

  2. Invariant monitoring is a key idea in secure program synthesis. The hackathon produced a PDF with a clear workflow diagram. Unfortunately the code and presentation links 404 -- perhaps a permissions problem. My scores reflect that, I couldn't see the work, and the write-up unfortunately didn't give enough to go on, beyond the diagram, on this occasion. This said, invariant monitoring is a genuinely worthwhile direction, and I'd encourage the entrant to keep developing it.

Cite this project

@misc{pattnayak2026invariant,
  title = {{Invariant Extraction + Monitoring}},
  author = {Debabrata Pattnayak},
  year = {2026},
  month = may,
  note = {Submitted to The Secure Program Synthesis Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/invariant-extraction-monitoring-5u2y}},
  url = {https://apartresearch.com/sprints/projects/invariant-extraction-monitoring-5u2y}
}

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