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Sprint projectNov 23, 2025México

Mechanistic Watchdog

Luis Cosio · Team SL5

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

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Report: Mechanistic Watchdog

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Mechanistic Watchdog is a mechanistic-interpretability-based “cognitive kill switch” for language models. Instead of only filtering final text, we monitor a model’s internal activations in real time and learn linear “concept vectors” that capture truthfulness and high-risk domains. Using datasets like Facts-True-False, TruthfulQA and WMDP-Bio, we calibrate a deception / misuse direction in the residual stream of mid-layers (e.g., Llama-3.1-8B, Qwen-2.5-3B). During generation, the watchdog projects each new token’s hidden state onto these vectors, smooths the scores, and halts the model if the trajectory crosses a learned threshold—interdicting deceptive or bio-risky cognition before it fully materializes in text.

Our experiments show that a single truthfulness vector trained on generic true/false facts generalizes out-of-distribution to TruthfulQA, cleanly separating truthful controls from misconceptions and factual lies. We also prototype a bio-defense profile that reliably detects when the model is “in a biological regime” and are iterating toward a contrastively trained safe-vs-misuse bio probe. Mechanistic Watchdog is intended as a building block for def/acc: an internal-state monitoring layer that defenders can place in front of powerful models to reduce the risk of AI-enabled deception and assist in catching early signs of bio- or cyber-misuse.

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Does this reduce AI-related catastrophic or existential risks?

Scoring guide
  1. 1Minimal Impact. The project has minimal relevance to AI safety. It doesn't meaningfully address risks uniquely enabled or accelerated by advanced AI systems.
  2. 2Tangential Connection. The project touches on AI safety concepts but lacks depth or specificity. The connection to AI-enabled threats (bio, cyber, or AI misuse) is weak or unclear.
  3. 3Clear AI Safety Value. The project clearly reduces AI-related risks with valuable contributions. It addresses specific threats from AI systems and engages meaningfully with biosecurity, cybersecurity, or AI safety challenges.
  4. 4Significant Impact Potential. The above, plus the project demonstrates scalable safety mechanisms or defensive approaches. It shows clear potential to buy time for solving harder problems like alignment, or creates positive externalities for the broader AI safety ecosystem.
  5. 5Major Advancement. The above, plus the project represents a significant leap forward in defensive AI safety. Judges would eagerly share this with biosecurity, cybersecurity, or AI safety researchers and expect it to influence the field.

Does this strengthen the shield against AI-enabled threats?

Scoring guide
  1. 1Minimal Relevance. The project is only tangentially related to defensive technology or societal protection. Connection to biosecurity, cybersecurity, or defensive infrastructure is unclear or missing.
  2. 2Some Relevance. The project has some relevance to defensive acceleration, but the connection is broad or generic. It touches on defense without specific focus on AI-enabled threats or protective capabilities.
  3. 3Clear Relevance. The project clearly addresses defensive gaps against AI-enabled threats. It connects to at least one track (biosecurity, cybersecurity, or defense infrastructure) and demonstrates understanding of the threat landscape.
  4. 4Strong Contribution. The above, plus the project builds on existing defensive approaches and offers novel tools, frameworks, or implementations. It explicitly explains how it strengthens defensive capabilities with realistic deployment potential.
  5. 5Breakthrough Impact. The above, plus the project provides breakthrough insights or tools that could significantly influence defensive technology development. It identifies critical gaps and presents compelling solutions with clear paths from prototype to deployed system.

Did you build something that actually works?

Scoring guide
  1. 1Incomplete or Flawed. The project appears rushed or incomplete. Technical implementation is flawed, core functionality doesn't work, or the approach is fundamentally unsound. Little to no documentation.
  2. 2Basic Competence. The project shows reasonable effort with basic technical competence. Core functionality partially works. Documentation exists but may be incomplete. Some limitations are acknowledged.
  3. 3Solid Hackathon Project. The project is technically solid and well-scoped for 48 hours. Core functionality works and is documented. Code/methods are understandable and limitations are honestly addressed. This is what a good weekend prototype should look like.
  4. 4Impressive Implementation. The above, plus the implementation exceeds typical hackathon quality. Clear methodology, thorough documentation, and working demo. The tool/prototype is immediately useful for defenders and could realistically be built upon.
  5. 5Exceptional Execution. The project far exceeds expectations with exceptional technical execution. The implementation is elegant, fully functional, and includes something special (e.g., deployed demo, exceptional documentation, innovative architecture, or clear startup potential).

  1. Impressive amount to build as a single person in such a short time!

    For future work, I'd love to see more exploration on stress testing this method with more prompts that intentionally try to jailbreak the models to see whether this manages to properly detect the flagged behaviour.

  2. Report provides a clear path to deployment. The prototype’s results are encouraging across models and tasks, and the performance overhead seems acceptable. I appreciate the clear explanation of where this tool sits in the broader AI-safety ecosystem.

    I would like to see more early thinking on robustness against adversarial adaptation, as motivated actors may learn to hide or route around these internal signals.

    It would also be helpful to discuss risks from accidental trigger events in high-stakes settings. If the system halts or blocks a model at the wrong moment, could that itself cause harm?

Cite this project

@misc{cosio2025mechanistic,
  title = {{Mechanistic Watchdog}},
  author = {Luis Cosio},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/mechanistic-watchdog-klay}},
  url = {https://apartresearch.com/sprints/projects/mechanistic-watchdog-klay}
}

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