UMER is a Unified Multimodal Embedding and Ranking framework that combines contrastive embeddings with Pair‑Aware Discriminative Reasoning to improve universal multimodal retrieval. It replaces item‑wise reflection with pair‑wise comparison of query–candidate pairs, enabling explicit identification of matching and discrepancy evidence. A mutual distillation strategy transfers reliable pairwise preferences between the embedding and ranking components, and UMER achieves state‑of‑the‑art performance on the MMEB‑V2 benchmark while supporting budget‑adjustable inference.
By Libiao Chen, Xiyang Liu, Yanheng Wei, Tao Wang, Zhenyu Tang
The paper introduces PILOT, a framework that treats reasoning as a latent variable to enhance multimodal embeddings. By modeling prior and posterior reasoning modes with a shared large language model, PILOT uses relational supervision during training to guide exploration of retrieval‑relevant reasoning traces. On the MMEB‑V2 benchmark, PILOT outperforms baselines across all task groups, notably improving visual‑document retrieval scores by over six points.
By Tianyu Yang, Yijun Tian, Lars Benedikt Kaesberg, Jan Philip Wahle, Bela Gipp, Terry Ruas, Daniel Kurzawe
arXiv:2606.13061v3 Announce Type: replace
Abstract: Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. D...
By Peixi Wu, Biao Yang, Feipeng Ma, Bosong Chai, Bo Lin, Wei Yuan, Fan Yang, Tingting Gao, Hebei Li, Xiaoyan Sun
The paper investigates whether reasoning always benefits universal multimodal embeddings (UMEs). By comparing the discriminative and reasoning-driven branches of UME-R1, the authors find that while reasoning improves positive similarity in 56.6% of cases, it also creates 15.7% false-helpful instances where hard negatives are drawn closer. Diagnostic analyses reveal that reasoning often de‑condenses retrieved neighborhoods and that chain‑of‑thought tokens encode evidence common to both positives and hard negatives. Based on these insights, the authors introduce SURE, a utility router that boosts UME-R1‑7B by 1.5 points and consistently improves other embedding models on MMEB‑V2 without retraining or extra VLM passes.
By Wenxiao Fan, Jingling Fu, Luohang Liu, Xinyuan Shan, Lichen Ma, Yu He, Junshi Huang, Yan Li, Kan Li
arXiv:2605.16638v2 Announce Type: replace
Abstract: Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this pa...
By Jianpeng Cheng, Xian Wu, Jiangfan Zhang, Wentao Bao, Chaitanya Ahuja, Shlok Kumar Mishra, Xuanming Cui, Hanchao Yu, Yang Gao, Fan Xia, Haixing Dai, Frankie Yuan, Zihao Wang, Xiaobing Chen, Qi Guo, Shaodan Zhai, Aashu Singh, Xiangjun Fan, Jun Xiao
VaME (Variational Multimodal Embeddings) introduces a framework that treats latent reasoning as a learnable distribution over trajectories, enabling autoregressive exploration in latent space guided by answer reconstruction. By augmenting embedding-token readout with a latent-fused embedding and optimizing over stochastic variational trajectories via reinforcement learning with a Semantic Decoding Reward, VaME achieves superior performance on the MMEB-V2 benchmark and reasoning-intensive tasks like MRMR. The method also delivers at least a 4.25× inference speedup compared to deterministic latent autoregressive baselines.
By Peixi Wu, Mingzhou Jiang, Feipeng Ma, Biao Yang, Yunhao Zhou, Wei Yuan, Bosong Chai, Huizu Lin, Jie Chen, Zhangchi Hu, Fan Yang, Wenwu Ou, Hebei Li, Xiaoyan Sun
arXiv:2604.22280v4 Announce Type: replace
Abstract: Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that...
By Peixi Wu, Ke Mei, Feipeng Ma, Bosong Chai, Zhibin Lan, Chenxi Zhao, Shannan Yan, Jie Chen, Zhangchi Hu, Yansong Peng, Bo Lin, Junjie Zhou, Dacheng Yin, Tianyi Wang, Fengyun Rao, Jing Lyu, Hebei Li, Xiaoyan Sun
arXiv:2607. 22643v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal input over a flat evidence space.
By Tianyu Yang, Shir Simon, Zhenzhen Li, Minhao Cheng, Xiangliang Zhang
arXiv:2609.05518v1 Announce Type: cross
Abstract: Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, m...
By Jiacheng Cai, Zijin Hong, Zheng Yuan, Huachi Zhou, Qinggang Zhang, Xiao Huang
MMEmb-R1 is a multimodal embedding framework that enhances reasoning by treating it as a latent variable and selecting beneficial reasoning paths through pair-aware selection and counterfactual intervention. It uses reinforcement learning to invoke reasoning only when necessary, reducing unnecessary computation and latency. On the MMEB-V2 benchmark, MMEmb-R1 achieves a state‑of‑the‑art score of 71.2 with just 4 B parameters.
By Yuchi Wang, Dingkang Yang, Haiyang Yu, Weikang Bian, Jiefeng Long, Xiao Liang, Chao Feng, Hongsheng Li
arXiv:2611.05193v2 Announce Type: replace
Abstract: Composed Image Retrieval (CIR) aims to retrieve a target image given a reference image and a modification text. Although recent approaches leveragi...
By WonJun Moon, Seongchan Kim, Dahun Kim, Seungryong Kim
arXiv:2411. 15041v2 Announce Type: replace Abstract: Advanced Multimodal Large Language Models (MLLMs) struggle with recent Knowledge-based Visual Question Answering (VQA) tasks, such as INFOSEEK and Encyclopedic-VQA, due to their limited and frozen knowledge scope, often leading to ambiguous and inaccurate responses.
By Tao Zhang, Ziqi Zhang, Zongyang Ma, Yuxin Chen, Zhongang Qi, Chunfeng Yuan, Bing Li, Junfu Pu, Yuxuan Zhao, Zehua Xie, Jin Ma, Ying Shan, Weiming Hu