Core Insights - The article discusses the limitations of multi-modal large language models (MLLMs) in spatial reasoning, highlighting their inability to effectively understand and visualize spatial concepts, leading to a phenomenon termed "visual illiteracy" [2][3]. Group 1: Challenges in Spatial Reasoning - Spatial reasoning is identified as a core cognitive ability for humans to understand three-dimensional structures, which poses a significant challenge for MLLMs in practical applications [2]. - Current methods primarily rely on "language description tuning," which fails to provide models with a true visual understanding of spatial concepts [2][3]. Group 2: Introduction of MILO - A research team has proposed MILO (Implicit Spatial World Modeling) to address the spatial reasoning challenges faced by MLLMs by integrating visual generative feedback with symbolic reasoning [4]. - MILO employs a two-phase training process: the first phase involves visual generative tuning where the model learns spatial transformations through visual outputs, and the second phase involves language tuning using spatial instruction data [5]. Group 3: Enhancements in Geometric Perception - To further enhance geometric perception, the team introduced RePE (Relative Positional Encoding), which captures relative transformations between adjacent frames instead of relying on a global coordinate system, improving generalization and adaptability across datasets [8][9]. Group 4: GeoGen Dataset - The research team constructed the GeoGen dataset, comprising approximately 2,241 videos and 267,000 "observation-action-result" triplets, aimed at enhancing geometric perception generation [10]. - The dataset includes diverse sources such as scanned 3D scenes and internet videos, ensuring a wide range of realistic scenarios [11]. Group 5: Validation of MILO - The effectiveness of MILO was validated across multiple baseline models and five categories of spatial understanding tasks, achieving optimal performance in 3D scene understanding tasks and spatial reasoning tasks [12][16]. - Notably, MILO improved accuracy by 3.2% in the ScanRefer task and achieved an average accuracy of 61.7% in the VSI-Bench spatial reasoning task, surpassing the baseline VG-LLM by 2.2% [16].