ChronoPhyBench：多模态大语言模型真正理解世界还是仅仅利用语言先验？
ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
AI总结：
提出ChronoPhyBench基准，通过视频上下文和文本描述强制模型进行物理状态推理，揭示当前开源模型物理推理能力仍处初级阶段。
摘要：
多模态大语言模型（MLLMs）的最新进展在开放世界推理和理解方面展现了卓越的能力。然而，一个关键的不确定性仍然存在：这些模型是真正融合跨模态信息以构建基于物理的推理链，还是仅仅利用强大的语言先验来掩盖单模态依赖，从而幻觉出高级多模态能力？受此启发，为严格减轻语言模态偏差和捷径，我们提出一个新的多模态时序物理动力学推理基准ChronoPhyBench，该基准将下一状态预测与视觉问答（VQA）范式统一，通过基于历史视频上下文和文本描述，强制模型通过单图像选择以及更复杂的多帧时序排序任务来推断后续物理状态。同时，我们构建了一个基于ChronoPhyBench标准的大规模多模态推理数据集，包含超过10,000个长视频及其精心标注的描述，总计500万token。我们的实验评估揭示了与以往基准结论的鲜明对比。当前开源模型执行基于物理的多模态推理的能力仍处于初级阶段。最终，本工作旨在系统性地压力测试多模态模型的推理能力，量化幻觉率，并推动物理AI的发展，从而为社区提供一个朝着通用人工智能（AGI）的稳健且透明的评估框架。
英文摘要：
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity persists: it remains unclear whether these models genuinely synthesize cross-modal information to construct physically grounded reasoning chains, or if they merely exploit strong language priors to mask single-modality reliance, thereby hallucinating advanced multimodal capabilities. Motivated by this, and to rigorously mitigate language modality bias and shortcuts, we propose a novel multimodal Chrono}logical Physical Dynamics Reasoning Benchmark ChronoPhyBench, which unifies next state prediction with Visual Question Answering (VQA) paradigms by conditioning on historical video context and textual captions to enforce models to deduce subsequent physical states through both single image selection and the inherently more complex task of multiple frame chronological sorting. Concurrently, we construct a large-scale multimodal reasoning dataset curated using the ChronoPhyBench criteria, comprising over 10,000 long-form videos paired with meticulously annotated captions, totaling 5M tokens. Our experimental evaluations reveal a stark contrast to conclusions drawn by previous benchmarks. The capacity of current open-source models to perform physically grounded multimodal reasoning remains in its infancy. Ultimately, this work seeks to systematically stress-test the reasoning capabilities of multimodal models, quantify hallucination rates, and advance the development of Physical AI, thereby providing the community with a robust and transparent evaluation framework toward Artificial General Intelligence (AGI).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
