Title: GAVEL: Towards Rule-Based Safety through Activation Monitoring
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: AI Safety, Activation-Based Monitoring, Rule-Based Detection, Large Language Models, Misuse Detection

Abstract: Large language models (LLMs) are increasingly paired with activation-based monitoring to detect and prevent harmful behaviors that may not be apparent at the surface-text level. However, existing activation safety approaches, trained on broad misuse datasets, struggle with poor precision, limited flexibility, and lack of interpretability. This paper introduces a new paradigm: rule-based activation safety, inspired by rule-sharing practices in cybersecurity. We propose modeling activations as cognitive elements (CEs), fine-grained, interpretable factors such as "making a threat" and "payment processing", that can be composed to capture nuanced, domain-specific behaviors with higher precision. Building on this representation, we present a practical framework that defines predicate rules over CEs and detects violations in real time using a multi-label classifier. This enables practitioners to flexibly configure and update safeguards without retraining models or detectors, while supporting transparency and auditability. We further release datasets, code, and an initial CE vocabulary with misuse rulesets to bootstrap community collaboration. Our results show that compositional, rule-based activation safety improves precision, supports domain customization, and lays the groundwork for scalable, interpretable, and auditable AI governance.

TL;DR: A Rule-Based Approach to Activation-Based AI Safety