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Sprint projectMar 23, 2026Lagos, Nigeria

TDMF: Trajectory Deviation Monitor with Forensic Deep-Dive

Abubakar Abdulfatah · Team Rakabuba

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: TDMF: Trajectory Deviation Monitor with Forensic Deep-Dive

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TDMF is a trajectory-aware AI safety monitor that detects coordinated multi-step attacks — sequences where each individual action is locally innocent, but the full trajectory constitutes a staged exploit (reconnaissance → payload → exfiltration).

Current action-only monitors score each step in isolation. TDMF maintains a rolling forensic audit ledger and scores actions against a constraint envelope (what the agent is categorically forbidden from doing), not a prediction of what it will do. When violations accumulate, a forensic deep-dive reconstructs causal chains across the full history.

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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 a cool idea and points out a genuine flaw with common monitors that aren't given context on future actions. These common monitors are shown all past actions up to the action so they aren't entirely stateless, and in LinuxArena, you need these action level monitors to handle live failure side tasks. But for the existing literature, this author points out a good point that having a monitor not see into the future when evaluating an action is missing alpha. Also they propose hierarchical monitoring which has been explored in the literature, but cool to see anyway.

    AI was used a bit to much to write the code and paper. It made the paper too long, with too much detail, and if you look at the codebase, the transcripts used to evaluate the monitor were hardcoded and very minimal: https://github.com/abubakar-xyz/tdmf-ai-control/blob/main/tdmf/eval/transcripts.py . Would have been cool to have seen this done with real agent transcripts (e.g. those available on linuxarena.ai).

    Read full reviewShow less

  2. Nice implementation of a multi-turn policy, and nice approach.

    One minor engineering nit: this is poorly documented, but Luthien policy's are (supposed to be) ~stateless, as the same policy can be acting on many requests at once and you usually don't want those results interfering with each other - there are ways to persist state across turns that are session-specific, but they're poorly documented. This is mostly a criticism of Luthien's documentation at the moment, rather than your implementation.

Cite this project

@misc{abdulfatah2026tdmf,
  title = {{TDMF: Trajectory Deviation Monitor with Forensic Deep-Dive}},
  author = {Abubakar Abdulfatah},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/tdmf-trajectory-deviation-monitor-with-forensic-deepdive-rmql}},
  url = {https://apartresearch.com/sprints/projects/tdmf-trajectory-deviation-monitor-with-forensic-deepdive-rmql}
}

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