视频推理：评估大语言模型如何提取、整合和重构时空证据
Reasoning over Video: Evaluating How MLLMs Extract, Integrate, and Reconstruct Spatiotemporal Evidence
AI总结：
本文评估大语言模型在视频中的时空推理能力，提出VAEX-BENCH基准，通过合成数据测试抽象推理任务，揭示模型在抽象任务中的局限性。
摘要：
随着具身代理研究的兴起，对时空视频理解的需求增加，但现有基准主要强调提取性推理。本文提出抽象时空推理的评估框架，构建可控的合成视频数据集，包含五种抽象推理任务及其提取性对应任务。通过实验比较了最先进的大语言模型在提取和抽象设置下的性能，揭示其在抽象任务中的限制，并提供对底层瓶颈的细致分析。
英文摘要：
The growing interest in embodied agents increases the demand for spatiotemporal video understanding, yet existing benchmarks largely emphasize extractive reasoning, where answers can be explicitly presented within spatiotemporal events. It remains unclear whether multimodal large language models can instead perform abstractive spatiotemporal reasoning, which requires integrating observations over time, combining dispersed cues, and inferring implicit spatial and contextual structure. To address this gap, we formalize abstractive spatiotemporal reasoning from videos by introducing a structured evaluation taxonomy that systematically targets its core dimensions and constructs a controllable, scenario-driven synthetic egocentric video dataset tailored to evaluate abstractive spatiotemporal reasoning capabilities, spanning object-, room-, and floor-plan-level scenarios. Based on this framework, we present VAEX-BENCH, a benchmark comprising five abstractive reasoning tasks together with their extractive counterparts. Our extensive experiments compare the performance of state-of-the-art MLLMs under extractive and abstractive settings, exposing their limitations on abstractive tasks and providing a fine-grained analysis of the underlying bottlenecks. The dataset will be released soon.
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。
