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arXiv · 2610.19564

Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning

Chunxi Luo·Qishuai Ding·Xingchen Peng·Hengwei Ye·Ke Zhang·Wenxin Lian·Tianyi Lu·Chunlong Yu·Zhenya Lin
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Abstract

A spatial answer depends on the reference frame specified in language and its supporting visual evidence. Item accuracy alone obscures whether correctness persists when either changes. We introduce Frame Evidence, a benchmark of 320 matched triplets and 960 Chinese multiple-choice questions from captured and rendered scenes. One family compares three grounded reference frames over fixed images. The other pairs image deletion annotated as insufficient with answer-preserving addition. Joint metrics require correct answers across all three frames and paired evidence variants. Across 20 configurations on 288 held-out questions, GPT three-shot reaches 50.00\% item accuracy, but the best three-frame group accuracy is 5/48 (10.42\%), reached by GPT two-shot. Few-shot prompting and task-specific adaptation improve item accuracy without consistently improving joint correctness. Input controls show where images support matched success, while repeated executions reveal answer instability beneath similar scores.

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Chunxi Luo, Qishuai Ding, Xingchen Peng, Hengwei Ye, Ke Zhang, Wenxin Lian, Tianyi Lu, Chunlong Yu, Zhenya Lin. 2026-10-14. Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning. https://arxiv.org/abs/2610.19564

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