Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for vision tasks, typically through fine-tuning to produce universal representations. However, their performance on video remains inferior to Video Foundation Models (VFMs). In this paper, we focus on leveraging MLLMs for video-text embedding and retrieval. We first conduct a systematic layer-wise analysis, showing that intermediate (pre-trained) MLLM layers already encode substantial task-relevant information. Leveraging this insight, we demonstrate that combining intermediate-layer embeddings with a calibrated MLLM head yields strong zero-shot retrieval performance without any training. Building on these findings, we introduce a lightweight text-based alignment strategy which maps dense video captions to short summaries and enables task-related video-text embedding learning without visual supervision. Remarkably, without any fine-tuning beyond text, our method outperforms current methods, often by a substantial margin, achieving state-of-the-art results across common video retrieval benchmarks.
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）
SpaceR: Reinforcing MLLMs in Video Spatial Reasoning
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（国家多媒体信息处理重点实验室，计算机学院，北京大学）；Nanyang Technological University（南洋理工大学）；WeChat AI, Tencent Inc., China（微信AI，腾讯公司，中国）
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）
