构建多智能体系统，让AI像裁判一样看懂犯规并给出解释。
SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing
- 用多智能体协作处理视觉与规则文本，跨模态检索增强推理
- 在1200+问题、600段视频上测试，准确率显著优于通用大模型
- 开源知识库和基准数据集，适合体育AI与可解释决策研究者
裁判工作在体育中至关重要，公平、准确且可解释的判罚是核心。尽管智能辅助技术已在足球裁判中广泛应用，但现有AI方法仍处于初级阶段，大多仅关注孤立的视频感知任务，缺乏对犯规情境的理解与推理能力。为填补这一空白，我们提出SoccerRef-Agents：一个全面且可解释的多智能体足球裁判决策框架。主要贡献包括：(i) 构建包含1200多个裁判理论问题和600个犯规视频片段的多模态基准SoccerRefBench；(ii) 基于最新《竞赛规则》和经典案例库构建向量化知识库RefKnowledgeDB，实现精准的知识驱动推理；(iii) 设计新型多智能体架构，通过跨模态RAG机制弥合视觉内容与法规文本之间的语义鸿沟。本研究探索了多模态大模型与裁判专业知识融合的技术能力，实验表明系统在判罚准确性和解释质量上均显著优于通用多模态大模型。所有数据库、基准与代码将公开发布。
原文摘要 · Abstract (English)
Refereeing is vital in sports, where fair, accurate, and explainable decisions are fundamental. While intelligent assistant technologies are being widely adopted in soccer refereeing, current AI-assisted approaches remain preliminary. Existing research mostly focuses on isolated video perception tasks and lacks the ability to understand and reason about foul scenarios. To fill this gap, we propose SoccerRef-Agents, a holistic and explainable multi-agent decision-making framework for soccer refereeing. The main contributions are: (i) constructing the multimodal benchmark SoccerRefBench with over 1,200 referee theory questions and 600 foul video clips; (ii) building a vector-based knowledge base RefKnowledgeDB using the latest "Laws of the Game" and a classic case database for precise, knowledge-driven reasoning; (iii) designing a novel multi-agent architecture that collaborates via cross-modal RAG to bridge the semantic gap between visual content and regulatory texts. This work explores the technical capability of integrating MLLMs with refereeing expertise, and evaluations show our system significantly outperforms general-purpose MLLMs in decision accuracy and explanation quality. All databases, benchmarks, and code will be made available.
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