model-grounded rag

To address core limitations of large language models (LLMs)—including hallucination, knowledge obsolescence, and poor domain adaptability—this work systematically advances the Retrieval-Augmented Structured (RAS) generation paradigm. We propose a multi-granularity knowledge acquisition mechanism integrating sparse, dense, and hybrid retrieval, coupled with text structuralization, taxonomy construction, knowledge embedding, and prompt-driven reasoning to enable efficient external knowledge retrieval, semantic alignment, and controllable integration. Crucially, we deeply embed structured modeling into the augmentation pipeline, enhancing factual accuracy, temporal freshness, and domain-specific competence of generated outputs. Our contributions include: (1) a unified methodological framework for RAS generation; (2) principled pathways toward multimodal, cross-lingual, and interactive augmented generation; and (3) empirically validated improvements in reliability and specialization across diverse domains. This work establishes foundational design principles and future research directions for next-generation RAS systems.

To address error propagation and answer bias arising from unidirectional retrieval-then-reasoning in multi-hop question answering, this paper proposes RetroRAG, the first framework introducing backtracking-style reasoning. Its core is an evidence backtracking mechanism: inferring entity-centric queries to dynamically revise retrieved evidence and reconstruct reasoning paths, enabling iterative refinement and dynamic reorganization of trustworthy evidence through coordinated multi-round retrieval-generation-evaluation cycles. This establishes a closed-loop “evidence curation–discovery–verification” process, substantially enhancing robustness and interpretability for complex reasoning. On mainstream multi-hop QA benchmarks, RetroRAG consistently outperforms existing RAG methods, achieving significant gains in answer accuracy—particularly under challenging conditions involving long reasoning chains and noisy evidence.

This work addresses the lack of a systematic taxonomy for retrieval-augmented generation (RAG) applications. We propose the first comprehensive, lifecycle-spanning classification framework for RAG applications. Methodologically, we introduce a novel four-stage iterative construction paradigm—comprising multi-round expert collaboration, systematic literature review, dimensional abstraction, and empirical validation—thereby filling a critical gap in classification research beyond the ACL community. The framework comprises five meta-dimensions and sixteen fine-grained dimensions, balancing structural clarity with extensibility. Empirical validation across education, healthcare, and legal domains demonstrates its effectiveness in supporting design decisions, technical evaluation, and cross-domain understanding of RAG applications. By providing a foundational taxonomic infrastructure, this work advances the engineering-oriented deployment and standardization of RAG systems.

This work addresses the challenges of ambiguous citation provenance and content redundancy commonly encountered in existing retrieval-augmented generation (RAG) systems during information integration. The authors propose a knowledge base construction approach grounded in Q&A nuggets, which leverages explicit question-answer semantics to guide information extraction, selection, and generation while preserving source attribution throughout the pipeline. Departing from conventional fuzzy clustering abstractions, the method employs interpretable Q&A fragments as structured intermediate representations, enabling end-to-end traceable reasoning and generation. Experimental results on the TREC NeuCLIR 2024 dataset demonstrate that the proposed approach significantly outperforms the state-of-the-art nugget-based RAG system, Ginger, in terms of nugget recall, density, and citation accuracy.

Existing RAG systems predominantly employ paragraph-level coarse-grained attribution, which compromises verifiability in long-document question answering. This work introduces ReClaim, the first framework to enable sentence-level fine-grained attribution, achieving per-sentence traceability through alternating generation of claims and their corresponding citations. Methodologically, ReClaim integrates instruction tuning, autoregressive interleaved decoding, and citation-aware decoding control within a RAG architecture to ensure precise, context-aware attribution. On long-document QA benchmarks, it achieves 90% citation accuracy—substantially outperforming state-of-the-art coarse-grained approaches—and demonstrates robustness and reliability in verifiable generation. The core contribution is the establishment of the first end-to-end verifiable generation paradigm that enforces strict claim–citation alignment, thereby advancing trustworthiness and interpretability in RAG-based systems.

Current information retrieval paradigms struggle to support complex analytical tasks such as trend analysis and causal inference, lacking end-to-end problem-solving capabilities, controllable reasoning processes, and verifiable results. This work proposes a novel paradigm termed “analytical search,” formally defining it as a distinct search type separate from traditional retrieval and retrieval-augmented generation (RAG). By explicitly modeling analytical intent, the approach constructs an evidence-driven, process-oriented, multi-step structured reasoning workflow. The study introduces a unified framework that integrates query understanding, recall-oriented retrieval, reasoning-aware fusion, and adaptive verification mechanisms. This framework lays the theoretical foundation and outlines future research directions for next-generation analytical search engines that are highly accountable and capable of supporting multi-objective analytical tasks.

This work addresses the reliability challenges of Retrieval-Augmented Generation (RAG) in biomedical and clinical question answering by proposing a novel RAG framework that integrates hybrid retrieval, re-ranking, and claim-level evidence verification. The system leverages Amazon Bedrock for document processing and retrieval, enhances evidence relevance through Amazon Titan Text Embeddings V2, OpenSearch Serverless, and Cohere re-ranking, and employs a dedicated judgment model to rigorously verify each generated claim against source evidence. Evaluated on 25 biomedical queries, the framework successfully grounded all 200 extracted factual claims in their original evidence sources out of 500 retrieved passages, achieving 100.0% grounding accuracy and substantially improving the factual reliability and verifiability of generated responses.

This study addresses the challenges of decision latency and information overload in space operations caused by the vast volume of technical documentation and scientific literature. It presents the first systematic evaluation of Retrieval-Augmented Generation (RAG) for this high-stakes domain, integrating multiple retrieval strategies, embedding models, and large language models to efficiently extract and synthesize actionable knowledge from domain-specific documents. Experimental results demonstrate that the proposed RAG pipeline substantially enhances the accuracy, relevance, and reliability of knowledge retrieval, thereby reducing decision uncertainty. The work delivers a practical and trustworthy intelligent support framework for complex space missions while delineating clear pathways for optimization and defining the boundaries of its applicability.

This study addresses a critical oversight in current retrieval-augmented generation (RAG) systems: their reliance on human-oriented document representations, which neglect the distinct representational needs of large language models as content consumers. Under fixed retrieval results, the authors systematically evaluate the impact of 14 document representation strategies—including selection, summarization, and rewriting—on question-answering accuracy across four generative models. Introducing answer retention rate as a novel metric to assess whether transformed documents preserve the correct answer, controlled experiments reveal for the first time that answer retention is the primary driver of generation accuracy, challenging prior assumptions that attributed performance gains to specific representational mechanisms. Notably, when answer retention is high, variations in wording, structure, length, or query dependence exert minimal influence on accuracy, underscoring that preserving answer information outweighs representational form.

This work addresses the limited interpretability of retrieval-augmented large language models (RAG). We propose the first rule-based explanation framework for RAG, generating human-readable *if-then* rules that capture statistical associations between the presence or absence of retrieved evidence and model outputs. Methodologically, inspired by frequent itemset mining, we design an Apriori-style pruning strategy that integrates exhaustive pattern enumeration with efficient constraint-driven pruning, ensuring semantic plausibility while substantially improving computational efficiency. Experiments across multiple RAG benchmark tasks demonstrate that our approach rapidly produces high-fidelity, interpretable rules—achieving superior explanation quality and up to 60% reduction in rule discovery overhead compared to baselines. The framework establishes a lightweight, transparent, and verifiable attribution paradigm for RAG systems.