TRACE: A Traffic Regulation Agent with Compositional Evidence

Traffic regulation compliance requires reasoning over temporal events, participant roles, thresholds, and exceptions across continuous driving processes. However, fixed regulatory specifications offer limited flexibility in handling context-dependent legal conditions, while LLM-based assessments with static context are constrained by the evidence provided upfront. We propose TRACE, a traffic regulation agent with compositional evidence that separates reusable evidence computation from regulation-specific reasoning. A stateful runtime maintains factual and temporal evidence, while Law Skills guide an event-centered workflow to acquire and compose the evidence needed for compliance assessment. To extend its regulatory coverage, TRACE supports constructing initial skills for new regulations and refining their evidence procedures through execution feedback within fixed legal and tool boundaries. TRACE achieves 96.67% strict accuracy on 751 real-world traffic regulation compliance cases. Starting from automatically initialized skills, evolution improves strict accuracy from 46.6% to 81.4% on held-out driving cases. These results demonstrate accurate traffic compliance assessment and the ability to improve regulation-specific evidence procedures through execution feedback.