RegGuard: AI-Powered Retrieval-Enhanced Assistant for Pharmaceutical Regulatory Compliance
- URL: http://arxiv.org/abs/2601.17826v1
- Date: Sun, 25 Jan 2026 13:11:39 GMT
- Title: RegGuard: AI-Powered Retrieval-Enhanced Assistant for Pharmaceutical Regulatory Compliance
- Authors: Siyuan Yang, Xihan Bian, Jiayin Tang,
- Abstract summary: RegGuard is an industrial-scale AI assistant designed to automate the interpretation of heterogeneous regulatory texts.<n>The system ingests heterogeneous document sources through a secure pipeline.<n>RegGuard improves answer quality specifically in terms of relevance, groundedness, and contextual focus.
- Score: 3.354018798133739
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The increasing frequency and complexity of regulatory updates present a significant burden for multinational pharmaceutical companies. Compliance teams must interpret evolving rules across jurisdictions, formats, and agencies, often manually, at high cost and risk of error. We introduce RegGuard, an industrial-scale AI assistant designed to automate the interpretation of heterogeneous regulatory texts and align them with internal corporate policies. The system ingests heterogeneous document sources through a secure pipeline and enhances retrieval and generation quality with two novel components: HiSACC (Hierarchical Semantic Aggregation for Contextual Chunking) semantically segments long documents into coherent units while maintaining consistency across non-contiguous sections. ReLACE (Regulatory Listwise Adaptive Cross-Encoder for Reranking), a domain-adapted cross-encoder built on an open-source model, jointly models user queries and retrieved candidates to improve ranking relevance. Evaluations in enterprise settings demonstrate that RegGuard improves answer quality specifically in terms of relevance, groundedness, and contextual focus, while significantly mitigating hallucination risk. The system architecture is built for auditability and traceability, featuring provenance tracking, access control, and incremental indexing, making it highly responsive to evolving document sources and relevant for any domain with stringent compliance demands.
Related papers
- From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning [81.97788535387286]
We propose a framework that internalizes the agentic verification-and-editing mechanism into a unified, single-pass inference process.<n>With minimal data, SRI-Coder enables Chat models to surpass the completion performance of their Base counterparts.<n>Unlike FIM-style tuning, SRI preserves general coding competencies and maintains inference latency comparable to standard FIM.
arXiv Detail & Related papers (2026-01-19T20:33:53Z) - Hybrid Retrieval-Augmented Generation Agent for Trustworthy Legal Question Answering in Judicial Forensics [30.232667436008978]
We present a hybrid legal QA agent tailored for judicial settings.<n>It integrates retrieval-augmented generation (RAG) with multi-model ensembling to deliver reliable, auditable, and continuously updatable counsel.
arXiv Detail & Related papers (2025-11-03T15:30:58Z) - Analyzing and Internalizing Complex Policy Documents for LLM Agents [53.14898416858099]
Large Language Model (LLM)-based agentic systems rely on in-context policy documents encoding diverse business rules.<n>This motivates developing internalization methods that embed policy documents into model priors while preserving performance.<n>We introduce CC-Gen, an agentic benchmark generator with Controllable Complexity across four levels.
arXiv Detail & Related papers (2025-10-13T16:30:07Z) - Domain-Specific Data Generation Framework for RAG Adaptation [58.20906914537952]
Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning power of large language models with external retrieval to enable domain-grounded responses.<n>We propose RAGen, a framework for generating domain-grounded question-answer-context (QAC) triples tailored to diverse RAG adaptation approaches.
arXiv Detail & Related papers (2025-10-13T09:59:49Z) - All for law and law for all: Adaptive RAG Pipeline for Legal Research [0.8819595592190884]
Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks.<n>This work introduces a novel end-to-end RAG pipeline that improves upon previous baselines.
arXiv Detail & Related papers (2025-08-18T17:14:03Z) - RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA [0.0]
Regulatory compliance question answering (QA) requires precise, verifiable information.<n>We present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG)<n>Our hybrid system outperforms conventional methods in complex regulatory queries, ensuring factual correctness with embedded triplets.
arXiv Detail & Related papers (2025-08-13T15:51:05Z) - Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation [69.45495166424642]
We develop a robust and discriminative QA benchmark to measure temporal, causal, and character consistency understanding in narrative documents.<n>We then introduce Entity-Event RAG (E2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping.<n>Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries.
arXiv Detail & Related papers (2025-06-06T10:07:21Z) - AI-Supported Platform for System Monitoring and Decision-Making in Nuclear Waste Management with Large Language Models [1.6795461001108096]
This paper presents a multi-agent Retrieval-Augmented Generation (RAG) system that integrates large language models (LLMs) with document retrieval mechanisms.<n>The system ensures regulatory decisions remain factually grounded, dynamically adapting to evolving regulatory frameworks.
arXiv Detail & Related papers (2025-05-27T20:29:53Z) - Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
arXiv Detail & Related papers (2025-04-21T04:56:47Z) - A Hybrid Approach to Information Retrieval and Answer Generation for Regulatory Texts [0.0]
This paper introduces a hybrid information retrieval system that combines lexical and semantic search techniques.<n>The system integrates a fine-tuned sentence transformer model with the traditional BM25 algorithm to achieve both semantic precision and lexical coverage.
arXiv Detail & Related papers (2025-02-24T01:16:16Z) - CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models [59.8529196670565]
CRAT is a novel multi-agent translation framework that leverages RAG and causality-enhanced self-reflection to address translation challenges.
Our results show that CRAT significantly improves translation accuracy, particularly in handling context-sensitive terms and emerging vocabulary.
arXiv Detail & Related papers (2024-10-28T14:29:11Z) - RIRAG: Regulatory Information Retrieval and Answer Generation [51.998738311700095]
We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages.<n>We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents.<n>We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
arXiv Detail & Related papers (2024-09-09T14:44:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.