用羽毛球双打诊断视觉语言模型的多智能体战术推理能力
DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton
- 以专业双打比赛为场景，分解关键瞬间测试模型战术理解
- 发现模型在空间状态和互动绑定上表现薄弱，整体准确率不足60%
- 提出轻量级校验器TacticCheck，无需训练即可提升推理一致性
视觉语言模型擅长描述静态场景，但在动态多智能体交互中表现不佳，其动作语义依赖角色协同与时空关联。本文提出多智能体战术推理的正式定义，并构建DoublesEval诊断框架，以专业双打羽毛球为结构化测试场景。该框架采用关键时刻协议，将回合拆分为战术关键节点，从原子识别、段内复合理解、段间因果推理到高层战术抽象四个维度评估模型。此设计可定位推理失败的具体环节，而非仅看答案对错。针对发现的缺陷，提出TacticCheck——一种无参数更新、无需真值标签的轻量级测试时一致性校验器，利用模型自身低层战术预测重排候选答案。在60个精选回合（约9.6K结构化实例）上零样本评估四款主流开源VLM，结果表明模型在所有诊断层级均表现薄弱，尤其在空间状态、互动绑定和终局证据方面存在明显瓶颈。TacticCheck在各模型上均带来稳定提升，但仍留有显著差距。研究强调需建立结构化、交互感知的评估范式以推动下一代视觉语言模型发展。代码已开源。
原文摘要 · Abstract (English)
Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated roles and spatial-temporal dependencies. We formalize this capability as \textbf{multi-agent tactical reasoning} and introduce \textbf{DoublesEval}, a diagnostic evaluation framework that leverages professional doubles badminton as a structurally tractable testbed. DoublesEval employs a key-moment-based protocol that decomposes rallies into tactically salient instants and probes models across four interpretable dimensions: atomic recognition, intra-segment composite understanding, cross-segment causal reasoning, and high-level tactical abstraction. This design isolates \emph{where} reasoning fails, rather than merely measuring answer correctness. To address observed failure modes, we propose \textbf{TacticCheck}, a lightweight constraint-guided test-time consistency checker that reranks candidate answers using the model's own lower-level tactical predictions, requiring no parameter updates or ground-truth labels at inference time. Evaluating four representative open-source VLMs on 60 curated rallies (yielding $\sim$9.6K structured instances) via a zero-shot protocol, we find that models remain weak across all diagnostic levels, with especially clear bottlenecks in spatial state, interaction binding, and terminal evidence. TacticCheck delivers consistent gains across all evaluated models, while still leaving a substantial gap to robust tactical reasoning. These results highlight the need for structured, interaction-aware evaluation paradigms for next-generation VLMs. The source code is available in \href{https://github.com/Chengjt1999/DoublesEval}{\textcolor{blue}{our GitHub repository}}.
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