GraphCompliance is a structure‑aware framework that models regulatory documents as a Policy Graph and contextual information as a Context Graph of entity‑relation triples. It aligns these graphs via a Compliance Gate to identify relevant regulatory requirements and evaluate them against contextual evidence, thereby preserving structural dependencies and reducing terminology mismatch. Experiments on EU2UK, GDPR‑13, and CONTRACT datasets show higher Matthews correlation coefficients than competing methods, and ablation studies confirm the importance of each component.
arXiv:2604. 06173v2 Announce Type: replace-cross Abstract: Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning.
arXiv:2605.29742v2 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for regulatory compliance demands rigorous traceability via comprehensive citations across multi-tiered auth...
arXiv:2608.28607v1 Announce Type: new Abstract: Pharmaceutical sponsors developing a drug for both the United States and the European Union must reconcile guidance issued independently by the FDA and...
arXiv:2606. 03326v1 Announce Type: new Abstract: Compliance pipelines detect violations as transient query results and do not keep the violation itself as a persistent graph object with review state, affected entities, or audit history.
arXiv:2606. 12065v1 Announce Type: new Abstract: Automating compliance check for geometry-intensive regulations remains a significant technical bottleneck in Building Information Modeling (BIM), primarily due to the semantic disparity between high-level regulatory logic and structured IFC data.
arXiv:2608.15382v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy r...
arXiv:2608. 08146v1 Announce Type: new Abstract: The increasing complexity of enterprise business scenarios has promoted the widespread adoption of long SKILL documents in agent systems, posing new challenges for compliance detection: large models incur substantial inference costs, while small models may fail to maintain detection accuracy.
arXiv:2608. 15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty.
The paper examines how large language models (LLMs) can assist in creating regulatory compliance artifacts for EU sustainability and privacy laws, specifically Digital Product Passports (DPPs) under the Ecodesign for Sustainable Products Regulation and Data Protection Impact Assessments (DPIAs) under the General Data Protection Regulation. It investigates the effects of data extraction instructions and regulatory ambiguity on the quality and consistency of LLM-generated artifacts, benchmarking various models against manually crafted ground‑truth schemas. Findings indicate that looser guidelines, like those for DPIAs, demand more extensive prompts to achieve consistency, whereas stricter formatting rules for DPPs yield consistent outputs but may introduce hallucinations.
arXiv:2608. 03868v1 Announce Type: cross Abstract: Causal Discovery (CD) from observational data faces two fundamental challenges.
arXiv:2608. 09028v1 Announce Type: new Abstract: Institutional policies stay in natural language while the systems that check compliance demand machine-readable constraints.
arXiv:2607. 24563v1 Announce Type: new Abstract: Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fallible and are often stored without the evidence, provenance, and validation history needed to decide whether an individual mapping should be trusted.