On Verifiable Legal Reasoning: A Multi-Agent Framework with Formalized Knowledge Representations

• URL: http://arxiv.org/abs/2509.00710v1
• Date: Sun, 31 Aug 2025 06:03:00 GMT
• Title: On Verifiable Legal Reasoning: A Multi-Agent Framework with Formalized Knowledge Representations
• Authors: Albert Sadowski, Jarosław A. Chudziak,
• Abstract summary: This paper introduces a modular multi-agent framework that decomposes legal reasoning into distinct knowledge acquisition and application stages.<n>In the first stage, specialized agents extract legal concepts and formalize rules to create verifiable intermediate representations of statutes.<n>The second stage applies this knowledge to specific cases through three steps: analyzing queries to map case facts onto the schema, performing symbolic inference to derive logically entailed conclusions, and generating final answers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Legal reasoning requires both precise interpretation of statutory language and consistent application of complex rules, presenting significant challenges for AI systems. This paper introduces a modular multi-agent framework that decomposes legal reasoning into distinct knowledge acquisition and application stages. In the first stage, specialized agents extract legal concepts and formalize rules to create verifiable intermediate representations of statutes. The second stage applies this knowledge to specific cases through three steps: analyzing queries to map case facts onto the ontology schema, performing symbolic inference to derive logically entailed conclusions, and generating final answers using a programmatic implementation that operationalizes the ontological knowledge. This bridging of natural language understanding with symbolic reasoning provides explicit and verifiable inspection points, significantly enhancing transparency compared to end-to-end approaches. Evaluation on statutory tax calculation tasks demonstrates substantial improvements, with foundational models achieving 76.4\% accuracy compared to 18.8\% baseline performance, effectively narrowing the performance gap between reasoning and foundational models. These findings suggest that modular architectures with formalized knowledge representations can make sophisticated legal reasoning more accessible through computationally efficient models while enhancing consistency and explainability in AI legal reasoning, establishing a foundation for future research into more transparent, trustworthy, and effective AI systems for legal domain.

Related papers

• LegalOne: A Family of Foundation Models for Reliable Legal Reasoning [54.57434222018289]
  We present LegalOne, a family of foundational models specifically tailored for the Chinese legal domain.<n>LegalOne is developed through a comprehensive three-phase pipeline designed to master legal reasoning.<n>We publicly release the LegalOne weights and the LegalKit evaluation framework to advance the field of Legal AI.
  arXiv  Detail & Related papers  (2026-01-31T10:18:32Z)
• Thinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning [11.255428720705204]
  We propose a framework that decomposes the process of identifying significant distinctions between cases into three-stage reasoning tasks.<n>Our framework models cases using factual predicates called factors, organizes them into a legal knowledge hierarchy, and defines verifiable rules for identifying distinctions.<n>We find that models consistently expend more computational resources on incorrect responses than correct ones, suggesting that "thinking longer" does not always mean "thinking smarter"
  arXiv  Detail & Related papers  (2025-10-09T18:15:28Z)
• Judicial Requirements for Generative AI in Legal Reasoning [0.0]
  Large Language Models (LLMs) are being integrated into professional domains, yet their limitations in high-stakes fields like law remain poorly understood.<n>This paper defines the core capabilities that an AI system must possess to function as a reliable reasoning tool in judicial decision-making.
  arXiv  Detail & Related papers  (2025-08-26T09:56:26Z)
• GLARE: Agentic Reasoning for Legal Judgment Prediction [60.13483016810707]
  Legal judgment prediction (LJP) has become increasingly important in the legal field.<n>Existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge.<n>We introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules.
  arXiv  Detail & Related papers  (2025-08-22T13:38:12Z)
• An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis [11.298720507764727]
  This research presents a framework combining prompt engineering with multidimensional knowledge graphs to improve legal dispute analysis.<n>Results show major improvements in sensitivity, specificity, and citation accuracy.
  arXiv  Detail & Related papers  (2025-07-10T16:22:41Z)
• Explainable Rule Application via Structured Prompting: A Neural-Symbolic Approach [0.0]
  Large Language Models (LLMs) excel in complex reasoning tasks but struggle with consistent rule application, exception handling, and explainability.<n>This paper introduces a structured prompting framework that decomposes reasoning into three verifiable steps: entity identification, property extraction, and symbolic rule application.
  arXiv  Detail & Related papers  (2025-06-19T14:14:01Z)
• CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
  We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
  arXiv  Detail & Related papers  (2025-06-05T17:02:52Z)
• RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models [58.69183479148083]
  Legal Judgment Prediction (LJP) is a pivotal task in legal AI.<n>Existing LJP models integrate judicial precedents and legal knowledge for high performance.<n>But they neglect legal reasoning logic, a critical component of legal judgments requiring rigorous logical analysis.<n>This paper proposes a rule-enhanced legal judgment prediction framework based on first-order logic (FOL) formalism and comparative learning (CL)
  arXiv  Detail & Related papers  (2025-05-27T14:50:21Z)
• An Explicit Syllogistic Legal Reasoning Framework for Large Language Models [5.501226256903341]
  Large language models (LLMs) can answer legal questions, but often struggle with explicit syllogistic reasoning.<n>We introduce SyLeR, a novel framework designed to enable LLMs to perform explicit syllogistic legal reasoning.<n>SyLeR employs a tree-structured hierarchical retrieval mechanism to synthesize relevant legal statutes and precedents.
  arXiv  Detail & Related papers  (2025-04-05T03:34:51Z)
• A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences [76.73731245899454]
  We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience.<n>Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision.<n>This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the Intelligent Court''
  arXiv  Detail & Related papers  (2025-03-02T10:26:54Z)
• Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning [19.477062052536887]
  We propose the Logical-Semantic Integration Model (LSIM), a supervised framework that bridges semantic and logical coherence.<n>LSIM comprises three components: reinforcement learning predicts a structured fact-rule chain for each question, a trainable Deep Structured Semantic Model (DSSM) retrieves the most relevant candidate questions and in-answer learning generates the final answer.<n>Our experiments on a real-world legal dataset QA-validated through both automated metrics and human evaluation-demonstrate that LSIM significantly enhances accuracy and reliability compared to existing methods.
  arXiv  Detail & Related papers  (2025-02-11T19:33:07Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval [16.29803062332164]
  We propose a few-shot approach where large language models assist in generating expert-aligned relevance judgments.<n>The proposed approach decomposes the judgment process into several stages, mimicking the workflow of human annotators.<n>It also ensures interpretable data labeling, providing transparency and clarity in the relevance assessment process.
  arXiv  Detail & Related papers  (2024-03-27T09:46:56Z)
• From LSAT: The Progress and Challenges of Complex Reasoning [56.07448735248901]
  We study the three challenging and domain-general tasks of the Law School Admission Test (LSAT), including analytical reasoning, logical reasoning and reading comprehension. We propose a hybrid reasoning system to integrate these three tasks and achieve impressive overall performance on the LSAT tests.
  arXiv  Detail & Related papers  (2021-08-02T05:43:03Z)

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.