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

Paper recorded by Signals 4 on 2026-10-08 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.CL · 自然语言处理 · first seen 2026-10-09

Abstract

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. We introduce SpaceCast-Bench, the first benchmark to directly and diagnosticall

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#283 most recent of 660 cs.CL papers we have recorded · ↑ newer: ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills · ↓ older: Latent Core Tokenizer: Compress, but Meaningfully
Cite this page: SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models: the #283 most recent of 660 cs.CL papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/spacecast-bench-evaluating-predictive-spatial-reasoning-in-vision-language-model.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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