Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence

Yanbing Zhang; Bo Wang; Jianhui Liu; Nan Jiang; Jiaxiu Jiang; Haoze Sun; Yijun Yang; Shenghe Zheng; Lin Song; Haoyang Huang; Nan Duan; Wenbo Li

arXiv · 2605.10588

Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence

Yanbing Zhang·Bo Wang·Jianhui Liu·Nan Jiang·Jiaxiu Jiang·Haoze Sun·Yijun Yang·Shenghe Zheng·Lin Song·Haoyang Huang·Nan Duan·Wenbo Li

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Abstract

Current Large Multimodal Models (LMMs) struggle with spatial reasoning tasks requiring viewpoint-dependent understanding, largely because they are confined to a single, static observation. We propose Thinking with Novel Views (TwNV), a paradigm that integrates generative novel-view synthesis into the reasoning loop: a Reasoner LMM identifies spatial ambiguity, instructs a Painter to synthesize an alternative viewpoint, and re-examines the scene with the additional evidence. Through systematic experiments we address three research questions. (1) Instruction format: numerical camera-pose specifications yield more reliable view control than free-form language. (2) Generation fidelity: synthesized view quality is tightly coupled with downstream spatial accuracy. (3) Inference-time visual scaling: iterative multi-turn view refinement further improves performance, echoing recent scaling trends in language reasoning. Across four spatial subtask categories and four LMM architectures (both closed- and open-source), TwNV consistently improves accuracy by +1.3 to +3.9 pp, with the largest gains on viewpoint-sensitive subtasks. These results establish novel-view generation as a practical lever for advancing spatial intelligence of LMMs.

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Yanbing Zhang, Bo Wang, Jianhui Liu, Nan Jiang, Jiaxiu Jiang, Haoze Sun, Yijun Yang, Shenghe Zheng, Lin Song, Haoyang Huang, Nan Duan, Wenbo Li. 2026-05-11. Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence. https://arxiv.org/abs/2605.10588

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