VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis

• URL: http://arxiv.org/abs/2605.22570v1
• Date: Thu, 21 May 2026 14:48:35 GMT
• Title: VGenST-Bench: A Benchmark for Spatio-Temporal Reasoning via Active Video Synthesis
• Abstract summary: VGenSTBench is a video benchmark that employs generative models to actively synthesize, highly controlled and diverse evaluation scenarios.<n>We establish a comprehensive 3x2x2 video taxonomy, encompassing Spatial Scale Scene, Perspective Dynamics to span diverse scenarios.
• Score: 23.34626996348395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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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