arXiv:2608.04011cs.CYcs.AI2026-08

用语义协议重构法律推理,让AI理解法律背后的动态逻辑

Towards a New Grammar of Reasoning for Artificial Legal Intelligence and the Mecelle as Its Semantic Protocol

  • 提出以本体为基础的Mecellem语义协议,重塑法律推理框架
  • 强调法律意义需动态构建,不能仅靠数据或统计分析
  • 适合研究AI法律系统、法律认知建模的研究者参考

本文审视人工智能引入背景下传统法律实践面临的认知与方法论危机,提出基于本体的Mecellem语义协议作为应对方案。分析指出,法律在保持规范一致性与适应社会变迁之间存在结构性张力,单纯依赖法典化、实证体系化或量化方法(如法律计量学)均不足。文章主张法律推理不能简化为数据检索或统计模式识别,而应建立在意义具情境性、需通过本体定义的实体类别与分层知识动态重构的基础上。在此视角下,Mecellem将法律重新构想为一个由本体、认识论与方法论轴线交织而成的动态架构,而非静态规则体系。从法律计量到语义协议的转变不仅是技术迭代,更是法律知识根基的重构。文章进一步指出,神经符号系统、知识图谱与代理型AI唯有嵌入此本体动态框架,方能有效应对持续存在的法律挑战。通过将法律视为持续生成领域而非已完成的理性整体,Mecellem为人类与机器层面的法律意义生产提供一种上下文敏感、可审计且一致的模型,为智能时代重思法律推理提供完整框架。

原文摘要 · Abstract (English)

This article examines the enduring epistemic and methodological crisis of traditional legal practice in light of the opportunities and constraints introduced by artificial intelligence. It proposes an ontologically grounded framework termed the Mecellem semantic protocol as a response to this crisis. The analysis focuses on the structural tension within law between maintaining normative coherence and adapting to evolving social and institutional conditions, and shows why approaches based solely on codification, positivist systematization, or quantitative methods such as jurimetrics are insufficient. The article argues that legal reasoning cannot be reduced to data retrieval or statistical pattern recognition. Instead, it is grounded in the premise that meaning is context-dependent and must be dynamically reconstructed through ontologically defined entity categories and differentiated layers of knowledge. Within this perspective, Mecellem reconceptualizes law not as a fixed system of rules, but as an ontodynamic architecture structured along the interconnected axes of ontology, epistemology, and methodology. The transition from jurimetrics to a semantic protocol is presented not merely as a technical shift, but as a transformation in the foundations of legal knowledge. The article further argues that neurosymbolic systems, knowledge graphs, and agentic artificial intelligence can effectively address persistent legal challenges only when embedded within such an ontodynamic framework. By understanding law as a domain of ongoing formation rather than a completed rational totality, Mecellem advances a context-sensitive, auditable, and coherent model for legal meaning production at both human and machine levels, offering a comprehensive framework for rethinking legal reasoning in the age of artificial intelligence.

法律AI语义协议本体论认知建模

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