Provably Safe Model Updates

2025-12-01

AI Summary

In safety-critical settings, continual model updating risks catastrophic forgetting or alignment drift, yet existing heuristic approaches lack formal safety guarantees. This paper introduces the first verifiable safe update framework, formulating the problem as computing the largest local invariant region in parameter space that satisfies given performance specifications. We propose a novel solution paradigm integrating abstract domain relaxation with dual optimization. Our method employs parameterized abstract domains—such as orthogonal intervals and zonotopes—to enable multi-domain parallelism, incorporation of regularization priors, and proactive use of future data—without relying on specific datasets or training algorithms. Experiments demonstrate that our approach matches or exceeds state-of-the-art heuristics in continual learning and large-language-model fine-tuning, while providing provable safety guarantees against specification violations induced by distributional shift.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Safety-critical environments are inherently dynamic. Distribution shifts, emerging vulnerabilities, and evolving requirements demand continuous updates to machine learning models. Yet even benign parameter updates can have unintended consequences, such as catastrophic forgetting in classical models or alignment drift in foundation models. Existing heuristic approaches (e.g., regularization, parameter isolation) can mitigate these effects but cannot certify that updated models continue to satisfy required performance specifications. We address this problem by introducing a framework for provably safe model updates. Our approach first formalizes the problem as computing the largest locally invariant domain (LID): a connected region in parameter space where all points are certified to satisfy a given specification. While exact maximal LID computation is intractable, we show that relaxing the problem to parameterized abstract domains (orthotopes, zonotopes) yields a tractable primal-dual formulation. This enables efficient certification of updates - independent of the data or algorithm used - by projecting them onto the safe domain. Our formulation further allows computation of multiple approximately optimal LIDs, incorporation of regularization-inspired biases, and use of lookahead data buffers. Across continual learning and foundation model fine-tuning benchmarks, our method matches or exceeds heuristic baselines for avoiding forgetting while providing formal safety guarantees.

Problem

• Certifying safe updates for dynamic machine learning models
• Computing largest locally invariant domains for performance specifications
• Providing formal safety guarantees in continual learning and fine-tuning

Innovation

• Formalizes safe updates via largest locally invariant domain
• Uses parameterized abstract domains for tractable primal-dual formulation
• Projects updates onto safe domain for efficient certification