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Sprint projectSep 14, 2026Lübeck, Germany

The Missing Process: Reconstructing Distributed Agent Activity from Partial Traces

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: The Missing Process: Reconstructing Distributed Agent Activity from Partial Traces

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When AI workflows span multiple agents, tools, messages, files, and services, the surviving trace may be incomplete. This project develops TPRN, the Temporal-Persistence Reconstruction Network, as an experimental programme for reconstructing typed process structure from partial traces. Rather than presenting a single successful architecture, it follows a sequence of experiments and audits that expose both useful relational signal and benchmark failures. Several apparently strong results collapse under simpler baselines or leakage checks, including a 424,625-task benchmark whose answer was always listed last. The main result is methodological: reconstruction accuracy can exceed reconstruction validity, so process reconstruction requires explicit controls, baselines, and uncertainty.

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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. Clear primary research question, could have been more thoughtful around the methods tested

  2. This is good work. The metrics have been carefully analyzed by the authors, and the code repo backs up the claims. One small issue noticed was working out the power floor on the concept null, saying up front that anything under about 2.2× ordinary drift wouldn't have shown up, so the claim is "no big conversion" rather than "no conversion"; this is how it is assumed that nulls should be reported.

  3. The paper's thesis is that a benchmark can make a method look good for reasons unrelated to the evidence, and that's true and worth saying. But it's a known problem wearing new clothes, and the positive side is empty: nothing the author built survived its own tests. The artifact transport failed, the calibration gate failed, the value-of-information policy didn't beat a simpler rule, and the best reconstruction result was matched exactly by a baseline with no learning in it. So the score reflects what's left standing, which is a lesson rather than a method.

    The lineage table and the audit-question table do a lot of work, and the honesty is easy to follow. What costs it is that the central object is hard to hold onto.

Cite this project

@misc{mariappan2026missing,
  title = {{The Missing Process: Reconstructing Distributed Agent Activity from Partial Traces}},
  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/the-missing-process-reconstructing-distributed-agent-activity-from-partial-traces-456z}},
  url = {https://apartresearch.com/sprints/projects/the-missing-process-reconstructing-distributed-agent-activity-from-partial-traces-456z}
}

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