The field of multimodal intelligence is moving towards more sophisticated and generalizable models, with a focus on integrating visual and linguistic understanding. Recent developments have shown that large-scale datasets and novel training methods can significantly improve the performance of multimodal models, enabling them to tackle complex tasks such as visual chain-of-thought reasoning and fine-grained image recognition. Notably, the introduction of VisReason, a large-scale dataset for visual chain-of-thought reasoning, has equipped multimodal large language models with more systematic and generalizable reasoning capabilities. Additionally, models like MammothModa2 and Percept-WAM have demonstrated strong performance in multimodal understanding and generation tasks, highlighting the potential of unified architectures and perception-enhanced models. Furthermore, techniques such as Chain-of-Visual-Thought and latent visual reasoning have shown promise in advancing visual reasoning and abstract visual thinking. Overall, the field is witnessing a shift towards more robust, efficient, and generalizable models that can effectively integrate multiple modalities and reason about complex phenomena. Noteworthy papers include VisReason, which introduced a large-scale dataset for visual chain-of-thought reasoning, and L2V-CoT, which proposed a novel training-free latent intervention approach for transferring chain-of-thought reasoning from language models to vision-language models.
Advancements in Multimodal Intelligence
Sources
VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning
L2V-CoT: Cross-Modal Transfer of Chain-of-Thought Reasoning via Latent Intervention
MammothModa2: A Unified AR-Diffusion Framework for Multimodal Understanding and Generation
DiVE-k: Differential Visual Reasoning for Fine-grained Image Recognition
Synthesizing Visual Concepts as Vision-Language Programs
Introducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding
Eliciting Chain-of-Thought in Base LLMs via Gradient-Based Representation Optimization
EEG-VLM: A Hierarchical Vision-Language Model with Multi-Level Feature Alignment and Visually Enhanced Language-Guided Reasoning for EEG Image-Based Sleep Stage Prediction
Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving
UniGame: Turning a Unified Multimodal Model Into Its Own Adversary
Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
SCoTER: Structured Chain-of-Thought Transfer for Enhanced Recommendation
Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment
Unifying Perception and Action: A Hybrid-Modality Pipeline with Implicit Visual Chain-of-Thought for Robotic Action Generation
Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving
HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generation
Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
Cross Domain Evaluation of Multimodal Chain-of-Thought Reasoning of different datasets into the Amazon CoT Framework
DeeAD: Dynamic Early Exit of Vision-Language Action for Efficient Autonomous Driving
Learning from Risk: LLM-Guided Generation of Safety-Critical Scenarios with Prior Knowledge
Monet: Reasoning in Latent Visual Space Beyond Images and Language
Qwen3-VL Technical Report