https://arxiv.org/pdf/2505.04921
This video gives an overview of the evolution of multimodal reasoning models in AI, drawing from a comprehensive survey paper. Here's a quick rundown:
• Introduction (0:00-2:04): The video introduces the concept of multimodal AI, which integrates information from various sources like images, videos, and text, to achieve a deeper, more human-like understanding. The survey paper that the video is based on aims to map out the progress and future directions in this field.
• Stage 1: Perception-Driven Modular Reasoning (2:35-3:40): Early approaches involved separate modules for different data types (e.g., images and text) that are later combined.
• Transformers and VLMs (3:41-4:20): The transformer architecture enabled better integration of modalities, leading to pre-trained vision language models (VLMs).
• Dual Encoder vs. Single Transformer Backbone (4:21-6:28): VLMs followed two main paradigms: dual encoder models using contrastive learning and single transformer backbone models for more direct interaction between modalities.
• Stage 2: Language-Centric Short Reasoning (6:37-8:11): This stage focuses on multimodal large language models (MLMs) using language as the central reasoning hub. Chain of thought (CoT) reasoning is introduced to improve the depth and reliability of the models.
• Three Approaches to Stage 2 (8:19-10:30): The video discusses prompt-based multimodal CoT, structural reasoning, and externally augmented reasoning.
• Stage 3: Language-Centric Long Reasoning (12:24-15:30): Stage three goes to the next level by focusing on more deliberate and compositional reasoning. Key areas include crossmodal reasoning, multimodal 01, and reinforcement learning from preference optimization (RLPO).
• Future Vision: Native Large Multimodal Reasoning Models (15:33-16:36): The ultimate goal is to create models where reasoning emerges natively from omnimodal perception and interaction, rather than being bolted onto a language model.
• Challenges and Benchmarks (16:57-18:56): The video concludes by highlighting the technical challenges in achieving NLMRMs, such as unified representations, learning from world experience, and data synthesis. It also emphasizes the importance of benchmarks and datasets for driving progress in the field.