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RAG Has Evolved into Multi-modal AI Search Agents (RL, HaPO)

2026-07-13 Science & Technology
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Description

Years ago we started with RAG and Vector Embeddings, then we moved to Agentic RAG systems and today we discover multi-modal AI Search Agents, which we train with SFT and RL (HaPO, a GRPO based optimization with step-level rewards) for optimal search results. RAG has a huge evolution trace. And today we look at SearchEyes. All rights w/ authors: SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation Zhengbo Jiao1, Yiming Cheng2, Yilei Jiang1, Kaituo Feng1, Rui Huang3, Tianyi Jiang4, Juanxi Tian5, Jiapeng Li2, Qunzhong Wang1, Tailai Chen6, Qianshan Wei6, Chuan Xiao8, Shanyu Rong4, Yangfu Li7, Yanhan Zhou2, Yunpu Ma9, Yifan Zhang6, Xiangyu Yue1,† from 1 MMLab, CUHK 2 THU 3 HKU 4 PKU 5 NTU 6 CASIA 7 ECNU 8 HIT 9 LMU #airesearch #aiexplained #aitechnology #aiagents #multimodalai

Top Comments (6)

@ABTalksOnAI 2026-07-15

The step-by-step reward idea was the most interesting part for me. It makes sense that teaching an AI how to reason through a problem would be more effective than only rewarding the final answer.

1
@JT91notabot 2026-07-13

Markov Effect ❤ Happy to see this in the wild

2
@timmygilbert4102 2026-07-13

I'm not yet in the rag-harness business yet, but that's cool, thanks for keeping me up to date.

0
@zakariaabderrahmanesadelao3048 2026-07-13

Those graphs are lookong neat

0
@vigfusg 2026-07-13

SearchEyes 27B is already at around avg 68.1 .. what would the tool use optimization realistically give us in increased performance ? Are we talking about 2-4 % or could we expect something substantially more ?

0
@tyoong719 2026-07-13

Can it make Baidu a better search engine perhaps overtaking Google's?

0

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