Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models

Mimo Shirasaka; Haochen Zhang; Yonatan Bisk

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arXiv · 2609.06880

Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models

Mimo Shirasaka·Haochen Zhang·Yonatan Bisk

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Abstract

Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated perspective. Humans infer such perspectives from shared environmental knowledge, activity context, and commonsense. Recent vision-language models (VLMs) appear capable of spatial reasoning, but their ability to infer a speaker's viewpoint from contextual cues and interpret situated spatial relations from that viewpoint remains unclear. We call this capability contextual observer grounding. To study this capability, we construct the Point-of-View Benchmark (POVBench), a dataset of 3D scenes and queries that disentangles Inferred, Stated, and Given forms of observer grounding in natural embodied communication. Given multi-view observations and a natural-language sentence, models must localize unseen or underspecified targets from situated spatial and contextual cues. Across state-of-the-art VLMs, localizing targets from directional language remains challenging, even when observer grounding is made explicit. We find that explicit breakdowns of observer-relative spatial reasoning improve target localization. Our project page is available at https://mimo-owl.github.io/POVBench/.

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Mimo Shirasaka, Haochen Zhang, Yonatan Bisk. 2026-09-07. Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models. https://arxiv.org/abs/2609.06880

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