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Sprint projectJun 13, 2025Joetsu City, Niigata Prefecture, Japan

Mapping the Narrow Path & Avoiding the Quicksand

Russell · Team JapanColorado

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: Mapping the Narrow Path & Avoiding the Quicksand

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Understand the current shortcomings of A Narrow Path, especially in policies 3-5, and work to address them.

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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. "Transfer Learning Standard Practice: Modern frontier model training

    begins with pre-trained weights, not zero initialization (Microsoft Learn,

    2024; SuperAnnotate, 2025)"

    This is false, however the following objection to requiring weights of zero is an excellent one that we should address.

Cite this project

@misc{russell2025mapping,
  title = {{Mapping the Narrow Path \& Avoiding the Quicksand}},
  author = {Russell},
  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/mapping-the-narrow-path-avoiding-the-quicksand-h6q1}},
  url = {https://apartresearch.com/sprints/projects/mapping-the-narrow-path-avoiding-the-quicksand-h6q1}
}

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