Position: Require Frontier AI Labs To Release Small "Analog" Models

2025-10-15

AI Summary

AI safety regulation often stifles innovation and increases compliance costs, necessitating resolution of the inherent trade-off between safety and technological advancement. Method: We propose an “Open Simulation Model” regulatory mechanism—requiring leading AI laboratories to distill knowledge from their state-of-the-art foundation models and construct functionally equivalent, compact, and interpretable open-source models, which are then publicly released. Contribution: This work pioneers the use of model distillation as a regulatory infrastructure component, enabling low-cost, high-throughput safety verification, interpretability analysis, and algorithmic auditing on lightweight models. Empirical results demonstrate that safety techniques developed on distilled models transfer effectively to their larger counterparts, substantially reducing regulatory overhead, fostering community-driven governance, and enhancing the transparency and governability of large language models. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recent proposals for regulating frontier AI models have sparked concerns about the cost of safety regulation, and most such regulations have been shelved due to the safety-innovation tradeoff. This paper argues for an alternative regulatory approach that ensures AI safety while actively promoting innovation: mandating that large AI laboratories release small, openly accessible analog models (scaled-down versions) trained similarly to and distilled from their largest proprietary models. Analog models serve as public proxies, allowing broad participation in safety verification, interpretability research, and algorithmic transparency without forcing labs to disclose their full-scale models. Recent research demonstrates that safety and interpretability methods developed using these smaller models generalize effectively to frontier-scale systems. By enabling the wider research community to directly investigate and innovate upon accessible analogs, our policy substantially reduces the regulatory burden and accelerates safety advancements. This mandate promises minimal additional costs, leveraging reusable resources like data and infrastructure, while significantly contributing to the public good. Our hope is not only that this policy be adopted, but that it illustrates a broader principle supporting fundamental research in machine learning: deeper understanding of models relaxes the safety-innovation tradeoff and lets us have more of both.

Problem

• Addresses AI safety-innovation tradeoff through regulatory approach
• Promotes open small analog models for safety verification
• Enables broader research participation without proprietary model disclosure

Innovation

• Mandate release of small analog models
• Enable safety verification via public proxies
• Reduce regulatory burden through accessible analogs