SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models

Key points

• Yet real-world spatial intelligence demands predictive spatial reasoning: constructing a scene from observations, anticipating how an intervention changes it, and reasoning about the unseen outcome.1
• We introduce SpaceCast-Bench, the first benchmark to directly and diagnostically evaluate this capability.1
• Built around an observe-transform-infer framework, its 3,862 questions from 182 real-world scenes span 16 task types at three levels: static perception, local prediction, and global prediction, progressively requiring scene understanding, spatial state updating, and relational inference over unobserved outcomes.1
• Controlled analyses further reveal that bridge views are critical for integrating distributed observations, and that explicit 3D evidence benefits models more reliably than generated outcome images or videos.1

Sources (2)

• [1]SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models
  Hugging Face Daily Papers · Oct 8, 12:00 AM

  Existing spatial reasoning benchmarks mainly test spatial perception: reading off relations already visible in the input.

  Yet real-world spatial intelligence demands predictive spatial reasoning: constructing a scene from observations, anticipating how an intervention changes it, and reasoning about the unseen outcome.

• [2]SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 05:44 PM · same content

Extractive summary: sentences quoted from the sources.