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Sprint projectMar 23, 2026West Lafayette

Denoising Trajectories as Early-Warning Signals for Diffusion Language Model Control

Seunghyun Yoo · Team Carter

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

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Report: Denoising Trajectories as Early-Warning Signals for Diffusion Language Model Control

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We study whether denoising trajectories in diffusion language models can serve as early warning signals for AI control. Using LLaDA with pretrained sparse autoencoders in an executable shell setting, we find trajectory-based monitoring detects attacks much earlier than output-only oversight, while steering shows weaker but preliminary promise for localized intervention.

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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. Using intermediary representations for monitoring in control and using steering is potentially interesting for control. Using diffusion LMs seems somewhat novel to me, however its difficult to see how this research is useful unless the most frontier models are also diffusion LMs.

    This would have been stronger if run in a control environment like BigCodeBench or APPS (both are single turn). It's also hard to tell if the SAEs are actually classifying scheming or the input prompt. Also I didn't see many statistical tests / p values and I don't see why safety is unchanged using output only monitor vs no monitor. Both would have made this paper stronger.

Cite this project

@misc{yoo2026denoising,
  title = {{Denoising Trajectories as Early-Warning Signals for Diffusion Language Model Control}},
  author = {Seunghyun Yoo},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/denoising-trajectories-as-earlywarning-signals-for-diffusion-language-model-control-31cb}},
  url = {https://apartresearch.com/sprints/projects/denoising-trajectories-as-earlywarning-signals-for-diffusion-language-model-control-31cb}
}

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