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

Haley Yi

Track: Track 2: Socio-Economical and Future Visions

Keywords: Regulation-Aware Legal Digital Twins, World Models, Counterfactual Analysis, Legal Knowledge Graph, Neuro-symbolic AI, Compliance, Contract Performance, Constraint Satisfaction, Explainable AI, Causal Inference, SMT Solvers, Process Mining, Conformal Prediction, Semantic Web, Digital Twin, Algorithmic Recourse, Traceability, Risk Management, Computational Law, Verifiable Explanation

TL;DR: We propose Regulation-Aware Legal Digital Twins (RALDTs), which couple legal knowledge graphs with learned world models to enable counterfactual contract analysis, verifiable compliance planning, and traceable explanation under constraints.

Abstract: 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.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~David_Scott_Lewis1

Format: Yes, the presenting author will definitely attend in person because they attending ICLR for other complementary reasons.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 48