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Sprint projectSep 14, 2026Lübeck, Germany

What Transfers Across Model Generators? Testing Process Representations Across DeepSeek, Claude, and GPT

Kishore Kumar Mariappan · Team TPRN

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

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Report: What Transfers Across Model Generators? Testing Process Representations Across DeepSeek, Claude, and GPT

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This project is a companion continuation of "The Missing Process: Reconstructing Distributed Agent Activity from Partial Traces", which develops the Temporal-Persistence Reconstruction Network (TPRN) programme. Here, we test whether process representations learned on one model generator retain signal when another generator realizes the same underlying synthetic process. We use 100 matched process cards independently realized by DeepSeek V3.2, Claude Sonnet 5, and GPT-5.6 Sol, producing 300 synthetic episodes. A compact flow/persistence representation achieves mean cross-generator balanced accuracy of 0.610, while a deterministic strict reference retains non-zero recall in every generator direction under a separate 1%-prevalence synthetic stress test. This is a new-generator, familiar-process test—not unseen-process generalization. A public Collusion Wiki ranking result is withheld after a tie-order audit, leaving real-trace transfer unresolved.

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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. Relevant question, and experiments, though could have been presented more clearly

Cite this project

@misc{mariappan2026transfers,
  title = {{What Transfers Across Model Generators? Testing Process Representations Across DeepSeek, Claude, and GPT}},
  author = {Kishore Kumar Mariappan},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/what-transfers-across-model-generators-testing-process-representations-across-deepseek-claude-and-gpt-0j7c}},
  url = {https://apartresearch.com/sprints/projects/what-transfers-across-model-generators-testing-process-representations-across-deepseek-claude-and-gpt-0j7c}
}

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