Jun 14, 2025
Red Teaming A Narrow Path - GeDiCa v2
Camille Truchot, Diego Dorn
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.
Tolga Bilge
Good identification of some potential weaknesses, such as not addressing hardware specifically. Includes sensible policy proposals to address weaknesses.
Tolga Bilge
Some criticisms I think didn't really make sense
Solutions are quite good
Cite this work
@misc {
title={
@misc {
},
author={
Camille Truchot, Diego Dorn
},
date={
6/14/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
