Jan 30, 2026
CoherenceGuard
Nadine Squires
Current AI governance relies on tracking physical infrastructure (data centers, GPU clusters, power consumption), but this approach fails against architecturally efficient "dark" systems that achieve frontier capabilities with minimal detectable footprint. CoherenceGuard solves this enforcement gap.
CoherenceGuard is a deterministic, open-source governance engine that audits AI systems at the structural level rather than the physical level, enabling enforceable international agreements even when traditional monitoring fails. It operates through three integrated layers:
1. Evidence Layer: Fixed-precision manifold mapping that generates bit-for-bit identical measurements across any hardware platform—eliminating "floating-point drift" that undermines legal defensibility.
2. Interpretation Layer: Swappable policy adapters that simultaneously benchmark systems against multiple standards (EU AI Act, G7 roadmaps, Apart Challenge protocols) using a single coherence-focused assessment.
3. Decision Layer: Automated enforcement triggers with immutable audit trails, providing the transparency required for whistleblower protections and international oversight.
This architecture operationalizes currently undefined "State-of-the-Art risk modeling" requirements, detects incremental capability gains that evade threshold-based alarms ("Boiling Frog" risk), and provides hardware-agnostic determinism for legally binding enforcement.
CoherenceGuard is fully prototyped with working code, tested on stealth architectures, and designed to provide the verification infrastructure for the 2027 binding agreement roadmap—directly advancing the challenge's goals of creating practical verification, compliance, and coordination tools for frontier AI safety.
The submission is very difficult to evaluate because the core claims are buried under dense, nonstandard jargon (e.g., "recursive geometric coherence," "paradox-preserving intelligence modeling," "entropy-aware degradation mechanics") that is not grounded in established literature. When checking the linked repositories, the technical claims are not reflected in the actual code. I'd suggest using standard terminology, grounding claims in existing work, and building a small working prototype that demonstrates one concrete capability.
I’m not in a position to evaluate the technical claims about ASIOS and CoherenceGuard, but I worry that you claim to be open sourcing very powerful capabilities which would have deleterious effects on the prospects for effective governance of frontier AI. You are relying on "International Binding Agreements" to enforce use, but your own technology (ASIOS) makes the physical detection of violators (via power/heat/compute) impossible. What prevents an actor from using ASIOS to build hidden AI on consumer hardware while simply deleting the CoherenceGuard auditing modules?
I guess that you are operating from an assumption that widely proliferated, highly capable AI systems will lead to desirable outcomes. This probably in turn rests on the idea that the dangers of misused or misaligned AI can be addressed “head on” through direct competition by others who have aligned AI and pursue widely beneficial aims, like defense. I think this is likely misguided and it relies on whether there will be methods of AI misuse which are highly offense-favored, in that their negative effects are difficult to defend against.
Cite this work
@misc {
title={
(HckPrj) CoherenceGuard
},
author={
Nadine Squires
},
date={
1/30/26
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
