arXiv:2607. 07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks.
By Haipeng Ding, Yuexiang Xie, Zhewei Wei, Yaliang Li, Bolin Ding
arXiv:2606. 05608v2 Announce Type: replace-cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
By Zhenfeng Cao
arXiv:2609.01045v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
By Enci Zhang, Haofeng Wang, Yuesheng Zhu, Xiaole Cui, Guibo Luo
arXiv:2606. 00189v1 Announce Type: cross Abstract: Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-shelf agentic patterns.
By Aditya Kumar, Zhihan Lei, Jerry Yan, Joshua W. Momo, Lauhitya Reddy, Rafael Enrique Cabrera Jimenez, Cassandra A. Cohen, Arthur Kajiyama, William W. Cohen
arXiv:2609.05774v1 Announce Type: new
Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...
By Katherine Tieu, Dongqi Fu, Yinglong Xia, Hong Li, Hong Yan, Jingrui He
The paper "It Takes Workflows to Evolve Better Workflows" introduces FloWright, a method that uses a hierarchical, structure‑aware reward system to allow one or more roles in a multi‑agent workflow to self‑evolve without extra models or data. It also proposes DataWright, an adaptive data hardening technique that transforms existing datasets into more challenging workflow‑level tasks. Experiments on document, slide, chart, code, math, and finance tasks show that small open models trained with FloWright can improve performance by up to +7.41%, with co‑evolving multiple roles yielding the largest gains.
"whyItMatters":"The work demonstrates that optimizing beyond the workflow generator—by enabling multiple agents to co‑evolve—can substantially enhance the effectiveness of multi‑agent workflows for complex real‑world tasks."
By Xuehang Guo, Haoyu Wang, Haifeng Chen, Yangyi Chen, Zhenhailong Wang, Qingyun Wang