Despite the exceptional reasoning capabilities of Multimodal Large Language Models (MLLMs), their adaptation into universal embedding models is significantly impeded by task conflict. To address this, we propose TSEmbed, a universal multimodal embedding framework that synergizes Mixture-of-Experts (MoE) with Low-Rank Adaptation (LoRA) to explicitly disentangle conflicting task objectives. Moreover, we introduce Expert-Aware Negative Sampling (EANS), a novel strategy that leverages expert routing distributions as an intrinsic proxy for semantic similarity. By dynamically prioritizing informative hard negatives that share expert activation patterns with the query, EANS effectively sharpens the model's discriminative power and refines embedding boundaries. To ensure training stability, we further devise a two-stage learning paradigm that solidifies expert specialization before optimizing representations via EANS. TSEmbed achieves state-of-the-art performance on both the Massive Multimodal Embedding Benchmark (MMEB) and real-world industrial production datasets, laying a foundation for task-level scaling in universal multimodal embeddings.
Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models
The Hong Kong Polytechnic University（香港理工大学）；Zhejiang University（浙江大学）；University of Electronic Science and Technology of China（电子科技大学）；Reallm Labs（Reallm 实验室）；Amazon（亚马逊）；The Hong Kong University of Science and Technology（香港理工大学）
Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization
Vermont Artificial Intelligence Lab, Department of Computer Science, University of Vermont（佛蒙特大学计算机科学系佛蒙特人工智能实验室）；Intelligent Machines Lab, Department of Artificial Intelligence, Information Technology University（信息技术大学人工智能系智能机器实验室）；Institute of Artificial Intelligence, University of Central Florida（中佛罗里达大学人工智能研究所）
From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
Institute of Digital Twin, Eastern Institute of Technology（东方理工数字孪生研究所）；Zhejiang University（浙江大学）；Munich Center for Machine Learning, LMU（慕尼黑机器学习中心，慕尼黑大学）
PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue
Paige, NYC, NY United States（美国纽约市帕伊公司）；Microsoft Research, Cambridge, MA United States（微软研究院）；Memorial Sloan Kettering Cancer Center, NYC, NY United States（纪念斯隆凯特林癌症中心）；University of Yale, New Haven, CT United States（耶鲁大学）
Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Cloud Computing（阿里云计算）；Hong Kong University of Science and Technology（香港科技大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion
Department of Computing, The Hong Kong Polytechnic University（计算系，香港理工大学）；Research Institute of Multiple Agents and Embodied Intelligence, Pengcheng Laboratory（多智能体与具身智能研究院，鹏城实验室）
