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arXiv · 2601.21081

Shape of Thought: Progressive Object Assembly via Visual Chain-of-Thought

Yu Huo·Siyu Zhang·Kun Zeng·Haoyue Liu·Owen Lee·Junlin Chen·Yuquan Lu·Yifu Guo·Yaodong Liang·Xiaoying Tang
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Abstract

Multimodal models for text-to-image generation have achieved strong visual fidelity, yet they remain brittle under compositional structural constraints, notably generative numeracy, attribute binding, and part-level relations. To address these challenges, we propose Shape-of-Thought (SoT), a visual CoT framework for process-supervised progressive shape assembly in the rendered 2D domain, without external engines at inference time. SoT trains a unified multimodal autoregressive model to generate interleaved textual plans and rendered intermediate states, helping the model capture shape-assembly logic without producing explicit geometric representations. Unlike text-only CoT, each decision is grounded in a rendered state, making counts, attachments, topology, and intermediate part-addition errors inspectable across the trajectory. To support this paradigm, we introduce SoT-26K, a large-scale dataset of grounded assembly traces derived from part-based CAD hierarchies, and T2S-CompBench, a benchmark for evaluating structural integrity and trace faithfulness. Fine-tuning on SoT-26K achieves 88.4% on component numeracy and 84.8% on structural topology, outperforming direct generation by +24.2 points on component numeracy and +19.3 points on structural topology. SoT establishes a transparent testbed for rendered-domain structure-aware generation. The code is available at https://github.com/yuhuo03/Shape-of-Thought.

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Yu Huo, Siyu Zhang, Kun Zeng, Haoyue Liu, Owen Lee, Junlin Chen, Yuquan Lu, Yifu Guo, Yaodong Liang, Xiaoying Tang. 2026-01-28. Shape of Thought: Progressive Object Assembly via Visual Chain-of-Thought. https://arxiv.org/abs/2601.21081

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