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Sprint projectJan 10, 2026Bouxières-Aux-Dames (France)

Nexus Station

Jean Charbonneau · Team Nexus Team

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

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Report: Nexus Station

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Nexus Station is a dual-architecture framework that relocates safety enforcement from the Model (Internal) to the Environment (External). By detecting adversarial intent in vector space before it reaches the LLM, we eliminate the "Refusal Tax" and enable models to remain uncensored, helpful, and sovereign, while maintaining strict safety boundaries.

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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. Interesting and promising idea, with some conceptual overlap with Anthropic’s constitutional-classifier line of work. The potential impact is clear, but key elements need refinement—particularly the definition and taxonomy of attack vectors. The execution also needs strengthening: the evidence base is currently limited (only 50 attack/benign examples), and the paper does not provide enough detail on how these vectors were generated, which makes it difficult to assess validity and reproducibility. Finally, the presentation needs improvement: the hackathon format was not followed, and the writing/structure made the paper harder to read and follow than necessary.

Cite this project

@misc{charbonneau2026nexus,
  title = {{Nexus Station}},
  author = {Jean Charbonneau},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/nexus-station-hvs0}},
  url = {https://apartresearch.com/sprints/projects/nexus-station-hvs0}
}

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