LM Fight Arena: Benchmarking Large Multimodal Models via Game Competition

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2510.08928 [cs.AI]

(or arXiv:2510.08928v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2510.08928 Focus to learn more arXiv-issued DOI via DataCite

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