Paper Detail
Jinho Park, Youbin Kim, Hogun Park, Eunbyung Park
Spatio-temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisely has become an essential challenge. However, existing spatio-temporal reasoning benchmark datasets primarily rely on static image sets or passively curated video data, which limits the evaluation of fine-grained reasoning capabilities. In this paper, we introduce VGenST-Bench, a video benchmark that employs generative models to actively synthesize highly controlled and diverse evaluation scenarios. To construct VGenST-Bench, we propose a multi-agent pipeline incorporating a human quality control stage, ensuring the quality of all generated videos and QA pairs. We establish a comprehensive 3x2x2 video taxonomy, encompassing Spatial Scale, Perspective, and Scene Dynamics to span diverse scenarios. Furthermore, we design a hierarchical task suite that decouples low-level visual perception from high-level spatio-temporal reasoning. By shifting the paradigm from passive curation to active synthesis, VGenST-Bench enables fine-grained diagnosis of spatio-temporal understanding in MLLMs.
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@misc{park2026vgenst,
title = {VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis},
author = {Jinho Park and Youbin Kim and Hogun Park and Eunbyung Park},
year = {2026},
abstract = {Spatio-temporal reasoning is a core capability for Multimodal Large Language Models (MLLMs) operating in the real world. As such, evaluating it precisely has become an essential challenge. However, existing spatio-temporal reasoning benchmark datasets primarily rely on static image sets or passively curated video data, which limits the evaluation of fine-grained reasoning capabilities. In this paper, we introduce VGenST-Bench, a video benchmark that employs generative models to actively synthesi},
url = {https://huggingface.co/papers/2605.22570},
keywords = {Multimodal Large Language Models, spatio-temporal reasoning, video benchmark, generative models, multi-agent pipeline, video taxonomy, hierarchical task suite, code available, huggingface daily},
eprint = {2605.22570},
archiveprefix = {arXiv},
}
{}