Title: DuoLLM: A Dual-Stream Decoupled Visual Language Model for 3D Spatial Reasoning
Primary area: foundation or frontier models, including LLMs
Keywords: 3D Spatial Reasoning, Large Vision-Language Models, Multi-modal learning, 3D Perception, Geometric Priors, Scene Understanding

Abstract: Recently,Large Vision-Language Models have achieved strong performance in visual recognition tasks, yet their capacity for 3D spatial reasoning remains limited. Such reasoning is critical for downstream applications, including scene understanding and robotic manipulation, where models must infer not only what objects are but also how they are arranged in space. Specificly,two factors contribute to this limitation: (i) a semantic bias, where object meaning tends to dominate over geometric structure, and (ii) the loss of depth cues when 3D environments are projected into 2D images. Consequently, current LVLMs often fail to generalize to tasks that require reasoning about inter-object relations in three dimensions.To address this,this paper propose DuoLLM, a multi-modal architecture that explicitly decouples semantic and spatial processing. The model adopts a two-stream design, where a dedicated spatial stream is equipped with a 3D perception engine. This engine combines (i) a depth estimation module that introduces 2.5D geometric priors for recovering lost geometric information, (ii) a relational attention mechanism that captures object-to-object spatial dependencies for overcoming semantic bias, and (iii) an asymmetric cross-attention module that fuses semantic and spatial features efficiently.On the challenging SpatialScore-Hard benchmark, DuoLLM delivers substantial improvements over widely used open-source LVLMs and approaches the performance of specialized SpatialAgent frameworks. These gains are consistently observed across diverse categories of 3D reasoning, highlighting the effectiveness of the proposed two-stream design and 3D perception engine. Together, these results suggest that explicitly modeling geometric priors provides a promising path to extend LVLMs beyond 2D recognition, advancing toward embodied AI systems capable of spatial reasoning in real-world environments.

TL;DR: We introduce DuoLLM, a dual-stream architecture with a 3D perception engine that explicitly decouples semantic and spatial processing to improve large vision-language models' 3D spatial reasoning capabilities.