English(EN) Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings

新的RIME框架通过优化生成和检索来增强多模态嵌入。

作者 PulseAugur 编辑部 · [2 个来源] · 2026-04-24 06:50

研究人员推出了一种名为重写驱动的多模态嵌入（RIME）的新框架，旨在增强生成式多模态嵌入。RIME通过一个检索友好的重写过程优化生成和嵌入，从而解决了思维链推理的局限性。该框架还整合了跨模态对齐（CMA）以连接生成式和判别式嵌入空间，并采用精炼强化学习（Refine-RL）使用稳定的语义锚点来指导优化。实验表明，RIME在缩短思考步骤长度的同时，性能优于现有的生成式嵌入模型。 AI

影响引入了一种新颖的生成式多模态嵌入方法，有望提高检索的准确性和效率。

排序理由这是一篇详细介绍生成式多模态嵌入新框架的研究论文。

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报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · 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 · 2026-04-27 04:00

Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings

arXiv:2604.22280v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that reasoning-driven generative multimodal embeddings can outperform discriminative embe…
arXiv cs.CV TIER_1 English(EN) · Xiaoyan Sun · 2026-04-24 06:50

Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings

Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that reasoning-driven generative multimodal embeddings can outperform discriminative embeddings on several embedding tasks. However, Chai…

报道来源 [2]

Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings

Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings

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