arXiv AI By Ruiyu Li, Haoyang Cai, Zhitong Guo, Tong Hu

MELLON - Multimodal Enhanced LLM for Online Navigation

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arXiv:2608. 09121v1 Announce Type: new Abstract: Web navigation agents are capable of addressing various types of tasks on different websites.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 1

MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents

arXiv:2508.13186v2 Announce Type: replace-cross Abstract: AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However...

By Shilong Li, Xingyuan Bu, Wenjie Wang, Jiaheng Liu, Jun Dong, Haoyang He, Hao Lu, Haozhe Zhang, Chenchen Jing, Zhen Li, Chuanhao Li, Jiayi Tian, Chenchen Zhang, Tianhao Peng, Yancheng He, Jihao Gu, Hui Huang, Donghao Zhou, Yuanxing Zhang, Jian Yang, Ge Zhang, Wenhao Huang, Zhaoxiang Zhang, Qiangpeng Yang, Shilei Wen
arXiv AI
Aug 24

A Survey on Foundations and Frontiers of Multimodal Agentic Frameworks: Techniques and Applications

The paper surveys how large multimodal models (LMMs) enhance agentic frameworks that combine perception, memory, reasoning, planning, and action. It examines the integration of multiple modalities—text, images, audio, and video—through delegated, late‑fusion, and early‑fusion architectures, and maps these designs to agent capabilities. The survey also reviews multimodal agentic systems in robotics, web navigation, multimedia content creation, and video understanding, evaluating performance, efficiency, and scalability trade‑offs.

By Neel Mokaria, Rishie Raj, Dheeraj Baiju, Xiaoqian Shen, Shraman Pramanick, Kevin Qinghong Lin, Arda Senocak, Mike Zheng Shou, Philip Torr, Mohamed Elhoseiny, Yapeng Tian, Ruohan Gao, Salman Khan, Sayan Nag, Sanjoy Chowdhury, Dinesh Manocha
arXiv AI
Jun 16

Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning

arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.

By Zhengbo Zhang, Changtao Miao, Jinbo Su, Zhaowen Zhou, Chunxia Zhang, Xukai Wang, Ruiqi Liu, Kaiyuan Zheng, Jiansheng Cai, Bo Zhang, Zhe Li, Shiming Xiang, Ying Yan