The field of multimodal reasoning and representation learning is rapidly evolving, with a focus on developing models that can effectively integrate and process multiple forms of data, such as text, images, and videos. Recent research has explored various approaches to improve multimodal understanding, including the use of vision-language models, graph-based methods, and reinforcement learning. Notably, the development of unified frameworks that can handle multiple tasks and modalities has become a key area of research, with models such as ThinkMorph and LongCat-Flash-Omni demonstrating impressive performance on a range of benchmarks. Furthermore, the importance of reciprocal cross-modal reasoning has been highlighted, with benchmarks such as ROVER and TIR-Bench providing a means to evaluate models' ability to reason across different modalities. Overall, the field is moving towards the development of more generalizable and interpretable models that can effectively capture complex relationships between different forms of data. Noteworthy papers include RzenEmbed, which introduced a unified framework for learning embeddings across multiple modalities, and UME-R1, which pioneered the exploration of generative embeddings. Additionally, the Agent-Omni framework has shown promise in enabling flexible multimodal reasoning without requiring costly fine-tuning.
Advancements in Multimodal Reasoning and Representation Learning
Sources
Enhancing Spatio-Temporal Zero-shot Action Recognition with Language-driven Description Attributes
RzenEmbed: Towards Comprehensive Multimodal Retrieval
ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning
LongCat-Flash-Omni Technical Report
CoT-Saliency: Unified Chain-of-Thought Reasoning for Heterogeneous Saliency Tasks
VinciCoder: Unifying Multimodal Code Generation via Coarse-to-fine Visual Reinforcement Learning
UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings
LIR: The First Workshop on Late Interaction and Multi Vector Retrieval @ ECIR 2026
TRISKELION-1: Unified Descriptive-Predictive-Generative AI
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval
Dynamic Multi-level Weighted Alignment Network for Zero-shot Sketch-based Image Retrieval
ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation
$\left|\,\circlearrowright\,\boxed{\text{BUS}}\,\right|$: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles
Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization
Vote-in-Context: Turning VLMs into Zero-Shot Rank Fusers
TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning
An Evaluation of Interleaved Instruction Tuning on Semantic Reasoning Performance in an Audio MLLM
CoCoVa: Chain of Continuous Vision-Language Thought for Latent Space Reasoning
LUMA-RAG: Lifelong Multimodal Agents with Provably Stable Streaming Alignment
UniChange: Unifying Change Detection with Multimodal Large Language Model
Beyond Single Embeddings: Capturing Diverse Targets with Multi-Query Retrieval
Agent-Omni: Test-Time Multimodal Reasoning via Model Coordination for Understanding Anything
What's in Common? Multimodal Models Hallucinate When Reasoning Across Scenes
Caption Injection for Optimization in Generative Search Engine
V-Thinker: Interactive Thinking with Images
Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm