arXiv:2606.29399v1 Announce Type: new Abstract: Reviewing nuclear regulatory documents requires multi-hop reasoning across tens of thousands of pages, where judgments depend on evidence assembled across multiple chapters. We frame this task as planning: an LLM-based agent observes the evidence collected so far, picks the next document fragment to inspect, and stops when the evidence is sufficient. The agent operates over a vectorless document tree using browse, read, and search tools, and maintains a dynamic knowledge graph (KG) as state. On a 200-question benchmark over NuScale Final Safety A
The increasing complexity and volume of regulatory documents, particularly in high-stakes sectors like nuclear energy, necessitates advanced AI methods for efficient review and compliance, pushing the development of sophisticated LLM-based agents.
This development showcases a critical application of AI for automating complex, multi-hop reasoning tasks in highly regulated environments, mitigating human error, and accelerating critical processes.
LLMs are evolving beyond simple question-answering to become autonomous agents capable of structured planning and knowledge graph construction for complex analytical tasks over massive, unstructured datasets.
- · AI agents developers
- · Nuclear energy sector
- · Regulatory bodies
- · Consulting firms
- · Manual document reviewers
- · Legacy compliance software vendors
Significant reduction in time and cost for reviewing complex regulatory and legal documents in specialized fields.
Increased efficiency and safety in highly regulated industries as AI agents can identify critical information and discrepancies more reliably.
The development of 'AI co-pilots' for knowledge workers in other document-heavy sectors, leading to a new wave of enterprise automation and skill displacement.
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Read at arXiv cs.AI