arXiv:2609.32192v2 Announce Type: replace
Abstract: Multi-agent workflows require task-relevant information to be shared across agents, while irrelevant, stale, unverified, or incompatible informatio...
By Sen Zhao, Ruiqi Kong, Zuyu Zhang, Lifeng Shen, Xinyu He, Ding Zou, Xu Zhang, Qinghua Zhang
Agensh is a new multi‑agent harness that eliminates a central orchestrator by letting workers self‑organize through a continuous cooperation loop. The system uses a shared workspace, message interface, and shared context to coordinate tasks, verify results, and merge progress asynchronously. Experiments on ProgramBench and pandoc show that scaling from 1 to 1,024 agents improves test‑pass rates by up to 49% relative, demonstrating that agent count is a viable scaling dimension for complex tasks.
By Zhihao Zhan, Ting Song, Li Dong, Shaohan Huang, Jianxun Lian, Yan Xia, Furu Wei
arXiv:2605. 17361v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) powered by large language models (LLMs) have emerged as a powerful paradigm for complex problem solving, where performance critically depends on the underlying inter-agent communication topology.
By Xuefei Wang, Jialu Wang, Fengbo Zhang, Yihan Hu, Di Zhang, Yutong Ye, Yikun Ban, Jun Han, Ruijie Wang
arXiv:2609.35965v1 Announce Type: cross
Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require t...
By Yunzhe Xu, Zhe Liu
MemMA is a plug‑and‑play multi‑agent framework that coordinates the memory cycle of memory‑augmented LLM agents on both forward and backward paths. On the forward path, a Meta‑Thinker guides a Memory Manager for construction and a Query Reasoner for iterative retrieval. On the backward path, MemMA performs in‑situ self‑evolving memory construction, generating probe QA pairs, verifying the memory, and converting failures into repair actions before finalization. Experiments on LoCoMo show that MemMA consistently outperforms existing baselines across multiple LLM backbones and improves three different storage backends.
By Minhua Lin, Zhiwei Zhang, Hanqing Lu, Hui Liu, Xianfeng Tang, Qi He, Xiang Zhang, Suhang Wang
arXiv:2607. 10350v1 Announce Type: new Abstract: Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution.
By Jiayi Tian, Shiao Liu, Yuting Xu, Jia Lu, Zihao Guan, Honglin Han, Di Yang, Minqi Gu, Yifei Qian, Tianlin Zhang, Yanqing Zhu, Zeqian Ye, Menglin Yang, Fei Wang, Xu Hu, Xiuxian Li, Wei Zhang, Shihui Su, Yiyan Ji, Jingbo Wang, Ziteng Feng, Jiaheng Liu, Zhaoxiang Zhang, Xiaolong Wu, Mingyang Yin, Zedong Chu, Mu Xu