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Sprint projectFeb 2, 2026Bangalore

Panopticon

Sanchayan Ghosh · Team Panopticon

Submitted to The Technical AI Governance Challenge. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Panopticon

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A proof of chain verifier for AI Use, determining if the AI has been tampered with and if so, at what stage.

Also detect anomalous outputs in the LLM, and flag them to the user, organizing the LLM into dangerous, confusing or safe inputs.

Analyze the LLM's activation states to determine LLM's confusion on seeing malicious prompts helping users craft more malicious prompts to analyze the LLM

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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. The project tries to address hardware provenance, activation-based safety monitoring, and automated red-teaming simultaneously. All of these are important topics but each of them is a major research area with substantial existing literature.

    My main suggestion would be to pick one problem and go deep. Try to understand what is known about the problem and where the real gaps lie. For example, for the activation monitoring direction (Layer 2) there exists a lot of highly relevant work on representation engineering and linear probes for safety-relevant features.

    The next most important thing is to take evaluation much more seriously: Evaluate whether your method achieves reasonable results and really stress-test your findings. A convincing evaluation of one component is better than a full-stack demo with lacking validation.

  2. The team built an impressive prototype. The core problem is that the system is not evaluated in any real way. My recommendation would be to pick the most promising component, run it against a concrete set of prompts, and show it catches something a simpler baseline doesn't.

Cite this project

@misc{ghosh2026panopticon,
  title = {{Panopticon}},
  author = {Sanchayan Ghosh},
  year = {2026},
  month = feb,
  note = {Submitted to The Technical AI Governance Challenge, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/panopticon-ja2g}},
  url = {https://apartresearch.com/sprints/projects/panopticon-ja2g}
}

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