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Sprint projectMay 25, 2026Toronto, Hosur, Tanjavur

Spec Triangulator: Multi-Tool Triangulation for Formal Specification Validation

Nikaran Kanchanadevi Marimuthu, Parthasaarathy Kamaraj, Vennila Kanchanadevi Marimuthu, Kishore Matheswaran · Team CornerCore AI

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

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Report: Spec Triangulator: Multi-Tool Triangulation for Formal Specification Validation

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Spec Triangulator is a two-component system designed to solve the triage and validation challenges in formal verification. While formal verification tools can confirm what a developer has specified, they cannot verify if the specification itself is semantically correct. Spec Triangulator helps engineers choose the right verification tools and validates that their specifications accurately capture the intended system behavior.

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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 is an interesting experiment, but I expect that in practice, agreement between tools on correctness/triangulation results are less determinant of the answer to "which tools should you use" than factors like robustness of the tool, traction in the community, usability, etc. An interesting related direction could be: which tools do agents *prefer* to use? What tools have the best "agent SEO"? This seems likely to determine the future ecosystem more than pure triangulation results, and could be an interesting direction for further work.

  2. This paper presents two contributions: the first is a classifier that given a project identifies a suite of verification tools to use for the project (such as TLC/Z3/Lean/Verus etc), and the second is a comparison of using an SMT solver against a model checker for verification. The first classifier is impressive in achieving a 93% accuracy (against a reference selection), although some caveats that I would consider about this result are that there is often not just one best tool/tools for a verification project, and often the best choice of tools depends on domain specific details (i.e like Rocq has a strong C compilation pipeline thanks to CompCert, so any project involving C programs would be best done in Rocq, lean has strong real probability theory, etc.). I don't doubt that an LLM could be good at making these decisions, but I am still skeptical of the utility of fine-tuning a particular model for this task. I would be curious to see a comparison to the frontier models for this task. I would expect Claude/ChatGPT to actually do tool selection fairly well out of the box. For the second project, the comparison of the two verification methods is interesting, though builds upon a rich prior work of invariant inference in distributed systems. I would be curious to see how this compares to techniques that combine both symbolic and model checking approaches, such as IVY, or any of the counter-example-guided-abstraction-refinement tools. I would also be curious if the authors had considered using Apalache, the symbolic model checker for TLC which might have elided the need to switch between/manually translate TLC to Z3.

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Cite this project

@misc{marimuthu2026spec,
  title = {{Spec Triangulator: Multi-Tool Triangulation for Formal Specification Validation}},
  author = {Nikaran Kanchanadevi Marimuthu and Parthasaarathy Kamaraj and Vennila Kanchanadevi Marimuthu and Kishore Matheswaran},
  year = {2026},
  month = may,
  note = {Submitted to The Secure Program Synthesis Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/spec-triangulator-multitool-triangulation-for-formal-specification-validation-1u47}},
  url = {https://apartresearch.com/sprints/projects/spec-triangulator-multitool-triangulation-for-formal-specification-validation-1u47}
}

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