arXiv:2606. 03143v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement.
By Jingbo Yang, Guanyu Yao, Yang Zhang, Ramana Rao Kompella, Gaowen Liu, Shiyu Chang
arXiv:2610.14428v1 Announce Type: new
Abstract: Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpor...
By Istok Bako, Bonan Min, Tiffany Shao, Claire Cheng, Yashar Mehdad
arXiv:2606. 01386v1 Announce Type: new Abstract: We present GuidaPA, a privacy-preserving chatbot for the Italian Public Administration (PA) trained via Federated Learning (FL) on documentation from two national PA platforms, SIGESON and SIDFORS.
By Daniel M. Jimenez-Gutierrez, Albenzio Cirillo, Raffaele Nicolussi, Alessio Beltrame, Andrea Vitaletti
arXiv:2608.21964v1 Announce Type: new
Abstract: Large language model (LLM) agents increasingly operate over evolving software repositories, where success depends on repository-specific procedural kno...
By Chenyuan Duan, Ge Shi, Zineng Mao, Ge Zhang, Hao Liang, Yinzhu Piao, Yuchen Wu, Zhixin Yao, Kaiyu Huang, Wenhao Huang, Linzhuang Sun, Shen Yan, Wentao Zhang
arXiv:2610.40758v1 Announce Type: new
Abstract: Self-evolving agents rewrite a persistent state from feedback, but it is largely untested whether this lets them learn a rule nobody told them. An agen...
By Chenjie Qiu, Boyan Xu, Shaobin Shi, Bingfeng Chen, Ruichu Cai, Dongsheng Luo
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