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Sprint projectJun 14, 2025Paris

Red Teaming A Narrow Path - GeDiCa v2

Camille Truchot, Diego Dorn · Team GeDiCa

Submitted to Red Teaming A Narrow Path: ControlAI Policy Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Red Teaming A Narrow Path - GeDiCa v2

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While the 'Narrow Path' policy confronts the essential risk of recursive AI self-improvement, its proposed enforcement architecture relies on trust in a fundamentally non-cooperative and competitive domain. This strategic misalignment creates exploitable vulnerabilities. Our analysis details six such weaknesses, including lack of verification, enforcement, and trust mechanisms, hardware-based circumvention via custom ASICs (e.g., Etched), issues with ‘direct uses’ of AI to improve AI, and a static compute cap that perversely incentivizes opaque and potentially risky algorithmic innovation. To remedy these flaws, we propose a suite of mechanisms designed for a trustless environment. Key proposals include: replacing raw FLOPs with a benchmark-adjusted 'Effective FLOPs' (eFLOPs) metric to account for algorithmic gains; mandating secure R&D enclaves auditable via zero-knowledge proofs to protect intellectual property while ensuring compliance; and a 'Portfolio Licensing' framework to govern aggregate, combinatorial capabilities. These solutions aim to participate in the effort to transform the policy's intent into a more robust, technically-grounded, and enforceable standard.

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Does the analysis realistically assess what government agencies, resources, and expertise would be needed to implement these policies? Are the identified implementation challenges specific and grounded in understanding of how similar policies have worked (or failed) in practice? Does the submission adequately consider bureaucratic, technical, and coordination complexities involved in enforcement? How well does the analysis account for real-world constraints like budget limitations, regulatory capture, and inter-agency coordination?

Does the analysis identify specific ways the policies could fail to prevent ASI development or be circumvented by determined actors? How thoroughly does the submission examine edge cases, loopholes, or unintended consequences that could undermine the 20-year goal? Does the assessment consider different threat models (state actors, rogue researchers, corporate actors) and how policies address each? Are the identified failure modes realistic and significant, or primarily theoretical edge cases?

Does the submission cite relevant historical examples of similar policies (nuclear non-proliferation, export controls, dual-use technology regulation) to support its arguments? Are claims backed by empirical data, documented case studies, or credible expert analysis rather than speculation? How well does the analysis draw lessons from comparable regulatory domains to assess likely outcomes? Does the submission avoid making unsupported assertions about what "would" or "could" happen without evidence?

  1. Some criticisms I think didn't really make sense

    Solutions are quite good

  2. Good identification of some potential weaknesses, such as not addressing hardware specifically. Includes sensible policy proposals to address weaknesses.

Cite this project

@misc{truchot2025red,
  title = {{Red Teaming A Narrow Path - GeDiCa v2}},
  author = {Camille Truchot and Diego Dorn},
  year = {2025},
  month = jun,
  note = {Submitted to Red Teaming A Narrow Path: ControlAI Policy Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/red-teaming-a-narrow-path-gedica-v2-bdjr}},
  url = {https://apartresearch.com/sprints/projects/red-teaming-a-narrow-path-gedica-v2-bdjr}
}

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