SpatialLogic-Bench: A Diagnostic Benchmark for Task-Oriented Spatiotemporal Reasoning
Xiaoda Yang, Shenzhou Gao, Can Wang, Jiahe Zhang, Menglan Tang, Jingyang Xue, Sheng Liu, Peijian Zhang
Abstract
Vision-Language Models (VLMs) have made significant progress in static perception, but their ability to understand dynamic task-oriented reasoning remains unclear. Existing benchmarks mainly focus on static spatial relationships and lack systematic assessment of dynamic reasoning capabilities. To this end, we propose SpatialLogic-Bench, a novel benchmark designed to evaluate VLMs’ understanding of spatiotemporal logic and their ability to assess task progress. The benchmark assesses two critical capabilities: first, fine-grained visual discrimination to accurately perceive subtle physical changes between state frames; second, the logical capacity to connect these changes to task goals and judge whether they indicate progress. To mitigate temporal dependency biases, we introduce a dual-task paradigm, presenting image pairs in both chronological and reversed orders while keeping task descriptions consistent. We construct a multi-scale evaluation system by varying time intervals between frames: smaller intervals test the model
BibTeX
@inproceedings{aaai2026_spatiallogicbenc,
title = {SpatialLogic-Bench: A Diagnostic Benchmark for Task-Oriented Spatiotemporal Reasoning},
author = {Xiaoda Yang and Shenzhou Gao and Can Wang and Jiahe Zhang and Menglan Tang and Jingyang Xue and Sheng Liu and Peijian Zhang and Yao Mu and Xiangyu Yue},
booktitle = {AAAI 2026},
year = {2026}
}