activity
20242026
most citedAdvances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

16 citations · 38 across the 6 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Harnessing Agentic Evolution

Jiayi Zhang, Yongfeng Gu, Jianhao Ruan +10

Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, evaluating them, and using fe…

cs.AI2025★ 16 cited

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Bang Liu, Xinfeng Li, Jiayi Zhang +45

The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated…

cs.AI2024★ 2 cited

SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning

Yizhou Chi, Yizhang Lin, Sirui Hong +9

Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks…

cs.AI2024★ 7 cited

AFlow: Automating Agentic Workflow Generation

Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu +11

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed i…

cs.AI2024★ 11 cited

Data Interpreter: An LLM Agent For Data Science

Sirui Hong, Yizhang Lin, Bang Liu +24

Large Language Model (LLM)-based agents have shown effectiveness across many applications. However, their use in data science scenarios requiring solving long-term interconnected t…