CoAL-RAG is proposed, a complexity-aware legal retrieval-augmented generation method, which constructs a multi-dimensional evaluation mechanism based on ``question essence'' and ``retrieval consistency'' to enable adaptive routing of retrieval strategies.
Abstract
Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions and poor interpretability for complex ones, making it difficult to meet the requirements for both answer quality and efficiency in high-risk scenarios. To address this issue, this paper proposes CoAL-RAG, a complexity-aware legal retrieval-augmented generation method, which constructs a multi-dimensional evaluation mechanism based on ``question essence''and ``retrieval consistency''to enable adaptive routing of retrieval strategies. First, the reasoning demand is quantified according to the logical structure of the question. Then, the discrepancy between semantic retrieval and keyword retrieval is utilized to indirectly reflect problem complexity, thereby selecting the most appropriate retrieval strategy and dynamically filtering contextual information. Experimental results demonstrate that the proposed method significantly outperforms baseline models not only on Chinese legal benchmarks (SocialLawQA, LawBench) but also demonstrates strong cross-jurisdictional generalization on English datasets (LexGLUE, CaseHold). Specifically, on Chinese datasets, the BLEU score improves by 42.5\% and ROUGE-L reaches 3.6 times that of knowledge graph-based methods. On English benchmarks, CoAL-RAG maintains highly competitive accuracy, achieving an optimal balance between generation quality, deep logical reasoning, and system efficiency across different legal systems.
Legal text generation and legal question-and-answer tasks impose stringent requirements on factual accuracy, evidence traceability and normative consistency. Conventional dynamic retrieval-augmented generation methods are difficult to directly adapt to legal task demands such as legal provision citation, terminology standardization and case evidence organization. Following the DRAGIN paradigm, this paper proposes LARIN (Lightweight Adapted Retrieval-Augmented Inference Network for Legal Issues), an adaptive retrieval-augmented reasoning framework tailored for legal scenarios. While maintaining the mainstream dynamic retrieval workflow, LARIN makes targeted optimizations in three key modules: retrieval triggering, query construction and evidence fusion. Specifically, LINDA identifies retrieval trigger points by comprehensively considering uncertainty, attention influence and semantic importance; JUDGE rewrites queries for legal terms and statutory expressions; MERF conducts relevance ranking, redundancy elimination and evidence fusion for legal provisions and case materials. In the CAIL2018 Chinese legal judgment prediction task, LARIN achieves a charge prediction exact-match accuracy of 0.3316, a micro-F1 score of 0.3837, and a precision of 0.3951, while the sentencing exact-match accuracy reaches 0.1575. Meanwhile, its average retrieval frequency is 1.81 and average token consumption stands at 373.15. Experimental results on CAIL2018 demonstrate that legal-oriented adaptation based on DRAGIN improves retrieval-augmented reasoning in this Chinese legal judgment prediction setting while maintaining low retrieval overhead. Cross-dataset robustness on additional legal QA or judgment benchmarks remains to be further validated.
This work proposes Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation (D2F-ReAG), a novel paradigm that adaptively controls reasoning depth by judging the reliability of the root-level reasoning.
Jiaoyang Li, Junhao Ruan, Shengwei Tang et al.· 0 citations
: The legal domain imposes unique demands on Large Language Models (LLMs), requiring high factual accuracy, precise statutory citation, and transparent reasoning. Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to mitigate hallucinations and improve knowledge grounding in legal applications. While general RAG systems have achieved success in other domains, legal scenarios present specific challenges due to the complexity of legal language, evolving jurisprudence, and jurisdictional variability. This survey provides a comprehensive overview of the technical evolution of legal RAG systems, categorizing existing approaches into four paradigms: Naive, Advanced, Modular, and Agentic RAG. Drawing on a broad set of recent legal Natural Language Processing (NLP) studies, we organize optimization strategies into three core stages of the RAG pipeline: Pre-retrieval, In-retrieval, and Post-retrieval. These stages highlight techniques adapted to legal use cases, such as document chunking, query rewriting, retrieval control, context filtering, and rationale-based response selection. By highlighting common architectural patterns and legal-specific adaptations, this survey aims to inform future research and practical deployment of robust, interpretable, and trustworthy legal LLMs.
Xin Li· Proceedings of the 3rd Inter...· 0 citations
This research paper proposes a Retrieval Augmented Generation framework that is specific to the legal field in order to assist interactive retrieval and reason about judgments from the Supreme Court of India and demonstrates strong performance on metrics including contextual recall and answer relevancy.
Sayed Ayaan Ahmed Sha, Sangeetha Sivanesan, A. Madasamy et al.· 0 citations
Graph-based and multimodal retrieval frameworks provide a strong foundation for long-document question answering, but single-pass retrieval can remain brittle when queries are ambiguous, multi-step, or misaligned with the indexed evidence. We present Agentic-RAG, a structure-aware retrieval-augmented generation framework that combines a MinerU-LightRAG-based document processing and graph retrieval pipeline with an LLM-based agentic query-control layer. The base pipeline supports structure-aware parsing and VLM-based captioning for textual, visual, and tabular evidence, while the agentic layer performs query planning, LLM-based evidence reranking, answer generation, groundedness checking, relevance checking, and query reformulation. The framework does not modify the underlying graph construction or indexing mechanism; instead, it improves retrieval control by guiding the base retriever toward evidence that better matches the user's information need. Experiments on HotpotQA and ASQA show that Agentic-RAG improves context precision on HotpotQA from 0.1682 to 0.2462 and substantially improves context precision and context recall on ASQA from 0.5045/0.2783 to 0.6522/0.4783. The results indicate that agentic query control is especially useful for ambiguity-heavy long-form reasoning, while sparse multi-hop evidence chaining remains a bottleneck for future work.
D. Lam, Gia Hien Tran, Tien-Dung Do· 2026 11th International Conf...· 0 citations
It is suggested that, in dense-urban POI settings where coordinates are reliable, the marginal benefit of explicit graph edges shrinks for coordinate-computable relationships, while structured spatial processing complements vector retrieval.
Noboru Otsuka· The International Archives o...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
