Keywords: legal ai, neuro-symbolic, world models, cross-border commerce, knowledge graphs, eu ai act, regulation, arbitration, trade finance, legal digital twins, work-in-progress
Paper Type: Short papers / work-in-progress
TL;DR: Progress report on regulation-aware neuro-symbolic legal world models that fuse legal graphs, simulations, and constraints to support explainable, compliant cross-border commerce and automated dispute resolution globally.
Abstract: This progress report proposes a research agenda at the intersection of Artificial Intelligence and Law (AI-LAW), neuro-symbolic AI (NeSy), and world models, focusing on the complex demands of international commercial practice. Cross-border transactions, AI-enabled supply chains, and arbitration increasingly rely on opaque foundation models, while emerging regulatory regimes, notably the EU AI Act, impose stringent risk-based obligations on high-risk AI systems. Purely neural models struggle to provide the explainability, reliability, and regulatory alignment required in these high-stakes environments. We argue that hybrid neuro-symbolic architectures and simulation-based world models offer a concrete pathway to more trustworthy legal AI. We introduce the concept of Regulation-Aware Neuro-Symbolic Legal World Models (R-NSLWMs): systems that integrate (i) extensive legal, regulatory, and contractual knowledge graphs aligned with benchmarks like LegalBench; (ii) domain-specific commercial world models (Legal Digital Twins) that simulate contractual performance and regulatory environments; and (iii) neuro-symbolic reasoning layers that enforce legal and physical constraints while generating human-legible explanations. We outline a four-layer architecture for R-NSLWMs, discuss use cases in AI-regulation-aware contracting and arbitration support, and propose a roadmap for developing the necessary benchmarks and methodologies. This agenda aims to bridge the gap between abstract regulatory principles and concrete technical implementations in global commerce.
Poster PDF: pdf
Submission Number: 57
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