Vid-LLM: A Compact Video-based 3D Multimodal LLM with Reconstruction-Reasoning Synergy

Recent advances in Large Language Models (LLMs) (V aswani et al., 2017; Radford et al., 2019; Naveed et al., 2025) and Multimodal Large Language Models (MLLMs) (Zhang et al., 2024a; Yin et al., 2024; Wu et al., 2023) have reinforced the paradigm of language as a universal interface, substantially improving cross-modal perception and reasoning. Extending this progress to 3D, recent research has focused on 3D-aware Multimodal Large Language Models (3D-MLLMs) (Ren et al., 2025), which unify 3D scene understanding and vision-language reasoning under a linguistic interface. This line of work underscores the importance of grounding language in persistent 3D spatial representations (Cheng et al., 2024a; Roh et al., 2022), offering a unified pathway toward systematic scene-level reasoning. Recent studies have made substantial progress in 3D vision-language (3D VL) reasoning (Chen et al., 2024c; Huang et al., 2023b), yet most approaches rely on complex 3D inputs, incurring high costs in data collection, preprocessing, and computation. Some models rely on point clouds or reconstructed scenes augmented with rendered views or semantic-geometric features (Hong et al., 2023a; Fu et al., 2024), while others adopt simpler inputs but still depend on explicit 3D scene representations such as reconstructed objects aligned with semantic representations (Chu et al., 2024; Huang et al., 2023a; 2024). Despite their effectiveness, these pipelines depend on depth, poses, or external modules, leading to substantial data and engineering overhead as well as high memory and latency costs.