Agent0 is a fully autonomous framework that enables large language model agents to evolve without external data by using a multi‑step co‑evolution process. It pits a curriculum agent against an executor agent, both derived from the same base LLM, where the curriculum agent creates increasingly challenging tasks and the executor learns to solve them. By integrating external tools into the executor’s workflow, the system creates a self‑reinforcing cycle that continuously generates high‑quality curricula, leading to significant gains in reasoning performance—an 18% improvement on mathematical reasoning and 24% on general reasoning for the Qwen3‑8B‑Base model.
By Peng Xia, Kaide Zeng, Jiaqi Liu, Can Qin, Fang Wu, Yiyang Zhou, Caiming Xiong, Huaxiu Yao
arXiv:2601.12538v2 Announce Type: replace-cross
Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) d...
By Tianxin Wei, Ting-Wei Li, Zhining Liu, Xuying Ning, Ze Yang, Jiaru Zou, Zhichen Zeng, Ruizhong Qiu, Xiao Lin, Dongqi Fu, Zihao Li, Mengting Ai, Duo Zhou, Wenxuan Bao, Yunzhe Li, Gaotang Li, Cheng Qian, Yu Wang, Xiangru Tang, Yin Xiao, Liri Fang, Hui Liu, Xianfeng Tang, Yuji Zhang, Chi Wang, Jiaxuan You, Heng Ji, Hanghang Tong, Jingrui He
Agentic University (AU) is a human‑participatory multi‑agent system that uses large language models to provide personalized tutoring, content generation, and adaptive learning paths. Learners act as organization members and collaborate with specialized agents to create and revise a Capability Program that links personal goals, learning activities, and layered assessment. Evaluation shows that AU improves program organization, assessment design, and revision quality, yielding higher assessment gains and stronger cognitive support compared to baseline approaches.
Agent skills are reusable procedural artifacts that extend language agents with specialized workflows, tool conventions, and domain behaviors at inference time. However, creating reliable skills still...
arXiv:2608. 04148v1 Announce Type: cross Abstract: Agentic AI is increasingly used to coordinate planning, implementation, review, and testing in software development, yet it often offers limited transparency into its decisions and interactions.
By Zihan Fang, Yueke Zhang, Yu Huang
The paper introduces a framework to evaluate large language models (LLMs) on their ability to conduct full scientific inquiry cycles, combining hypothesis generation, reasoning, and exploration. By extending the Countdown task into a mathematical environment, the authors assess how LLMs probe systems, collect observations, and infer relationships, examining exploration strategies and causal discovery. Experiments across seven LLMs and various prompting methods show limited inherent inquiry skills, but structured guidance inspired by educational scaffolding markedly improves performance.
By Marta Kne\v{z}evi\'c, Seyed Parsa Neshaei, Jibril Frej, Vinitra Swamy, Tanja K\"aser