English(EN) RACE: Learning Multimodal Search through Referential Abstraction and Compositional Evidence

新的RACE框架将多模态搜索代理的准确性提高了4.7%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员推出RACE，这是一个新颖的框架，旨在通过综合需要视觉基础的任务来训练多模态搜索代理。该方法采用参照抽象（RA）和组合证据（CE）来创建图像、目标和答案之间的依赖关系，从而加强视觉搜索和证据整合的监督。在RACE上进行微调后，Qwen3.5-27B模型在十个多模态基准测试中的准确性提高了4.7%，性能从53.5%提升到56.0%。该团队计划公开发布代码、数据和模型。 AI

影响增强了多模态搜索能力，有可能提高AI代理理解和与视觉信息交互的能力。

排序理由该集群描述了一篇介绍新颖框架并报告基准测试结果的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇介绍新颖框架并报告基准测试结果的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 2 days old; Coverage has settled into its steady-state source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Eric Tao, Minhao Sun, Liyan Liu, Zhijing Wu, Xiaohan Yu, Lang Mei, Li Huang, Chao Feng, Jinchao Ma, Sichen Kang, Siyu Mo, Chong Chen · 2026-10-21 04:00

RACE：通过参照抽象和组合证据学习多模态搜索

arXiv:2610.28382v1 Announce Type: new Abstract: Training multimodal search agents requires tasks that make visually grounded search necessary for determining the answer. Textual identity clues and unused target evidence allow shortcuts in synthetic questions, weakening supervisio…

本文如何被排名

报道来源 [1]

RACE：通过参照抽象和组合证据学习多模态搜索

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