Regulation-Aware Legal Digital Twins: Constrained World Models for Counterfactual Contract Performance, Compliance, and Damages

Post-deployment autonomous and agentic systems increasingly act inside socio-technical ecosystems (supply chains, trade finance networks, infrastructure projects) where factual dynamics and legal requirements are intertwined. Current LLM-centric legal tooling largely treats compliance as text generation, and therefore struggles to ground counterfactual analyses (“would this have been a breach?”) or produce verifiable explanations under regulation. We propose Regulation-Aware Legal Digital Twins (RALDTs): constrained world models that link (i) a multi-jurisdiction legal knowledge graph to (ii) a learned commercial world model and (iii) a neuro-symbolic constraint layer used during simulation, planning, and explanation. We formalize the interface, identify a key bottleneck—mapping latent trajectories to legally salient facts—and present an implementation strategy that combines event-graph extraction with solver-guided consistency repair. Finally, we define benchmark tasks (counterfactual breach, regulation-aware planning, and explanation faithfulness) with metrics for constraint satisfaction, causal validity, uncertainty calibration, and traceability.