用打斗游戏测试大模型实时决策能力，更真实地评估AI策略思维。
LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition
- 让大模型在《真人快打2》中互相对战，通过实时画面理解选招
- 6个主流模型在相同角色下对战，胜率差异反映真实策略水平
- 适合关注AI交互能力、游戏智能的开发者与研究者
现有大模型多模态评估常无法反映其在实时对抗环境中的表现。本文提出LM Fight Arena，一个基于经典格斗游戏《真人快打2》的新评测框架，要求模型实时解析游戏画面与状态数据，做出战术决策。在受控锦标赛中，六款主流开源与闭源模型以相同角色对战，确保公平性。该框架实现全自动、可复现、客观的动态评估，全面检验模型的战略推理能力。本工作构建了一个兼具挑战性与趣味性的评测体系，弥合了AI评估与互动娱乐之间的鸿沟。
原文摘要 · Abstract (English)
Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce LM Fight Arena (Large Model Fight Arena), a novel framework that evaluates LMMs by pitting them against each other in the classic fighting game Mortal Kombat II, a task requiring rapid visual understanding and tactical, sequential decision-making. In a controlled tournament, we test six leading open- and closed-source models, where each agent operates controlling the same character to ensure a fair comparison. The models are prompted to interpret game frames and state data to select their next actions. Unlike static evaluations, LM Fight Arena provides a fully automated, reproducible, and objective assessment of an LMM's strategic reasoning capabilities in a dynamic setting. This work introduces a challenging and engaging benchmark that bridges the gap between AI evaluation and interactive entertainment.
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