4 papers · 1 filter
EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle
Rong Wu, Xiaoman Wang, Jianbiao Mei +8
Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from their own experiences. While existing fr…
RE-Searcher: Robust Agentic Search with Goal-oriented Planning and Self-reflection
Daocheng Fu, Jianbiao Mei, Licheng Wen +11
Large language models (LLMs) excel at knowledge-intensive question answering and reasoning, yet their real-world deployment remains constrained by knowledge cutoff, hallucination,…
Learning on the Job: An Experience-Driven Self-Evolving Agent for Long-Horizon Tasks
Cheng Yang, Xuemeng Yang, Licheng Wen +9
Large Language Models have demonstrated remarkable capabilities across diverse domains, yet significant challenges persist when deploying them as AI agents for real-world long-hori…
GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents
Lingxiao Diao, Xinyue Xu, Wanxuan Sun +2
Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. Previous studies…