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Sprint projectSep 14, 2026Bogotá

Sensing Disruption: Output Entropy as a Channel-Agnostic Monitor for LLM Agents

Daniel Libardo Diaz Gonzalez, Roger Palomeque, Alejandro Sanchez Poveda, Sergio Montoya Ramirez, Juan Camilo Melgarejo Gómez · Team Harness Zero

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

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Report: Sensing Disruption: Output Entropy as a Channel-Agnostic Monitor for LLM Agents

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We proposed and implemented a monitoring system based on entropy to notice when an AI agent's uncertainty jumps because unsanctioned text reached it. It works for some models, and it's a first step toward monitoring that doesn't depend on reading logs or chain of thought.

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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. My favourite submission document!

    Even though the direct results are only applicable to systems using Qwen3, the general approach seems very promising. Perhaps expanded research in which many different measurements, ranging from entropy to residual-stream probes, are taken and compared with known communication patterns could uncover techniques that are useful across models.

  2. The paper evaluates context disruption using a two-agent setup. The paper needs to be exposed more to real world scenarios that involve multi-agent setup. It will help capture scale and complexity of the real world.

Cite this project

@misc{gonzalez2026sensing,
  title = {{Sensing Disruption: Output Entropy as a Channel-Agnostic Monitor for LLM Agents}},
  author = {Daniel Libardo Diaz Gonzalez and Roger Palomeque and Alejandro Sanchez Poveda and Sergio Montoya Ramirez and Juan Camilo Melgarejo Gómez},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/sensing-disruption-output-entropy-as-a-channelagnostic-monitor-for-llm-agents-n2j2}},
  url = {https://apartresearch.com/sprints/projects/sensing-disruption-output-entropy-as-a-channelagnostic-monitor-for-llm-agents-n2j2}
}

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