Jun 2, 2025
Leveraging Benford’s Law for Computational Complexity and Routing Transparency in AI Systems with Cryptographic Timestamps and Event-Triggered Actions
Aaron Goulden
This paper proposes an innovative method for embedding computational transparency into AI systems through cryptographic timestamping, Benford’s Law deviation analysis, and event-driven actions. By analyzing the deviation from Benford’s Law in timestamped logs, we derive insights into the computational complexity of query routing and the specific path a query took through a distributed network of trusted, semi-trusted, and untrusted nodes. This method not only enhances the traceability and legitimacy of AI outputs but also introduces the concept of triggering event-based actions, such as compensating analysts or deploying security teams, depending on the sensitivity of the models accessed. We discuss how this system ensures ethical data use, detects potential misuse of AI models, and provides an automated response to high-risk scenarios, offering a transparent and auditable framework for AI operations.
The ideas here seem to me to be independent of Expert Orchestration and largely independent of Artificial Intelligence. Parts of the framework reflect existing security practices.
The proposed extended cryptographically signed timestamps seem to reflect existing vendor software logs required in secure networks. These logs are copied in near-real-time from nodes to a read-only central repository to retain a clean copy in the face of later node log tampering.
If these cryptographically signed timestamps can be tampered with, I expect attackers would ensure that their tampering would not violate Benford’s law, to avoid easy detection.
Queries on sensitive data triggering security alerts is an existing feature of secure environments.
Seems like an interesting and novel way to analyze routing paths. This would be particularly relevant from the perspective of the AI Control framework, and it would be interesting to see it applied to that work.
The observation behind Benford’s law to track anomalies is interesting - its hard to validate without some sort of toy / soft simulation whether it holds. I would suggest starting with toy data and a family of models - the idea has good potential, but will perhaps be easier to investigate for single model behaviors first, and then extend to router families.
This paper proposes a framework but without any actual testing of the hypotheses that would support such a framework.
Cite this work
@misc {
title={
(HckPrj) Leveraging Benford’s Law for Computational Complexity and Routing Transparency in AI Systems with Cryptographic Timestamps and Event-Triggered Actio
},
author={
Aaron Goulden
},
date={
6/2/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
