Citation-Closure Retrieval and Per-Rule Attribution for Real-World Regulatory Compliance Question Answering
arXiv:2605.29742v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) for regulatory compliance demands rigorous traceability via comprehensive citations across multi-tiered authority structures. Unlike traditional multi-hop or legal QA, this task requires structured procedural lookups and evidence-set closure rather than entity resolution or case-law reasoning. Existing RAG systems struggle here due to flattened citation edges, fragmented retrieval expansions, and fragile post-hoc attribution. We formalize Regulatory Compliance QA with RegOps-Bench, a novel benchmark featurin
The increasing deployment of Large Language Models in sensitive, regulated domains is driving demand for rigorous, attributable, and auditable AI systems.
This development addresses a critical barrier to widespread LLM adoption in industries requiring strict compliance, enabling new applications and reducing legal and financial risks.
The focus shifts from general-purpose QA to specialized, procedural lookups and 'evidence-set closure' in RAG systems, demanding a new level of precision and traceability for AI outputs.
- · Regulatory compliance software vendors
- · Legal tech industry
- · Consulting firms specializing in RegTech
- · Financial services sector
- · General-purpose RAG system developers without specialized compliance features
- · Companies relying on opaque AI systems for regulatory tasks
- · Legal departments slow to adopt AI-powered tools
Specialized benchmarks and RAG architectures for regulatory compliance will become standard in enterprise AI deployments.
This rigorous attribution will accelerate LLM adoption in other high-stakes, regulated industries like healthcare and aerospace.
The development of 'Regulatory Compliance QA' as a distinct AI subfield could lead to new auditing standards and certification processes for AI systems.
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Read at arXiv cs.AI