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This paper proposes DiSR, a framework that separates 3D perception from symbolic reasoning by leveraging off-the-shelf vision models and fine-tuned LLMs rather than relying on massive end-to-end training. The approach demonstrates that explicitly reconstructing the physical world into structured geometric evidence significantly improves interpretability, modularity, and efficiency for spatial intelligence tasks. By decoupling these capabilities, researchers can build more scalable systems without needing complex tool-use policies or extensive 3D VQA datasets.
Disentangling 3D Modeling from Spatial Reasoning
arXiv:2608.05242v1 Announce Type: new Abstract: In this work, we explore an alternative paradigm for spatial reasoning by explicitly disentangling 3D perception from reasoning, rather than jointly acquiring implicit 3D perception and reasoning through large-scale training. Our key observation is that modern perception models excel at estimating continuous 3D geometry, whereas large language models (LLMs) are particularly effective at compositional and symbolic reasoning. Motivated by these complementary strengths, we propose the Disentangled Spatial Reasoner (DiSR), a simple yet effective framework that reconstructs the physical world into structured 3D evidence using off-the-shelf expert perception models and fine-tunes an LLM with LoRA to perform reasoning solely over this explicit geometric evidence. Without large-scale 3D VQA training or complex tool-use policies, DiSR achieves competitive performance on popular spatial reasoning benchmarks. Beyond its strong performance, DiSR offers improved interpretability, modularity, and computational efficiency, demonstrating that explicit separation of perception and reasoning is a scalable and effective alternative paradigm to end-to-end modeling for spatial intelligence.
Abstract:In this work, we explore an alternative paradigm for spatial reasoning by explicitly disentangling 3D perception from reasoning, rather than jointly acquiring implicit 3D perception and reasoning through large-scale training. Our key observation is that modern perception models excel at estimating continuous 3D geometry, whereas large language models (LLMs) are particularly effective at compositional and symbolic reasoning. Motivated by these complementary strengths, we propose the Disentangled Spatial Reasoner (DiSR), a simple yet effective framework that reconstructs the physical world into structured 3D evidence using off-the-shelf expert perception models and fine-tunes an LLM with LoRA to perform reasoning solely over this explicit geometric evidence. Without large-scale 3D VQA training or complex tool-use policies, DiSR achieves competitive performance on popular spatial reasoning benchmarks. Beyond its strong performance, DiSR offers improved interpretability, modularity, and computational efficiency, demonstrating that explicit separation of perception and reasoning is a scalable and effective alternative paradigm to end-to-end modeling for spatial intelligence.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2608.05242 [cs.LG]
(or arXiv:2608.05242v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.05242 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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