arXiv:2602. 00471v2 Announce Type: replace Abstract: While Visual Multi-Agent Systems (VMAS) promise to enhance comprehensive abilities through inter-agent collaboration, empirical evidence reveals a counter-intuitive "scaling wall": increasing agent turns often degrades performance while exponentially inflating token costs.
By Xinlei Yu, Chengming Xu, Zhangquan Chen, Bo Yin, Cheng Yang, Yongbo He, Yihao Hu, Jiangning Zhang, Cheng Tan, Xiaobin Hu, Shuicheng Yan
arXiv:2607. 09822v1 Announce Type: cross Abstract: Recognition tells an agent what is in an image; personal memory affects what is worth looking up next.
By Xiaofan Wu (Chance AI), Xi Zeng (Chance AI), Miaoxia Chen (Chance AI), Peishan Chen (Chance AI), Shuyan Li (Chance AI), Jiyun Yao (Chance AI), Hanyong Zhong (Chance AI), Jiahao Zhu (Chance AI)
arXiv:2606. 05008v1 Announce Type: cross Abstract: As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability.
By Jie Huang, Ruixun Liu, Sirui Sun, Xinyi Yang, Yin Li, Yixin Zhu, Yiwu Zhong
External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence. However, existing memory paradigms represent each memory item in raw text and image forms, so retrieval-based systems must pass the retrieved text or images to the generation LLMs/VLMs, resulting in high token consumption and storage pressure, making it unaffordable for resource-constrained applications.
arXiv:2606. 10572v1 Announce Type: new Abstract: External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence.
By Zhi Zheng, Ziqiao Meng, Hao Luan, Wei Liu, Wee Sun Lee
arXiv:2607. 11919v1 Announce Type: cross Abstract: Human memory is reconstructive, not a faithful recording.
By Xuguang Yu, Weigang Zheng, Minyue Yu