5 papers · 1 filter
SkillCAT: Contrastive, Assessment-Augmented and Topology-AwareSkill Self-Evolution for LLM Agents
Kunfeng Chen, Qihuang Zhong, Juhua Liu +1
Skill self-evolution methods for LLM agents aim to turn execution trajectories into reusable skill documents. However, current pipelines typically derive skill patches from a singl…
ConRAG: Consensus-Driven Multi-View Retrieval for Multi-Hop Question Answering
Yikai Zhu, Kunfeng Chen, Qihuang Zhong +2
Retrieval-augmented generation (RAG) has emerged as a promising paradigm for enhancing large language models (LLMs) on multi-hop question answering (QA), which requires reasoning o…
APEX-Searcher: Refining Credit Assignment with Subgoaling for Agentic Retrieval-Augmented Generation
Kun Chen, Qingchao Kong, Zhao Feifei +1
Retrieval-augmented generation (RAG) connects large language models (LLMs) to external knowledge, but single-round retrieval is often insufficient for complex multi-hop questions.…
Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model
Kunfeng Chen, Luyao Zhuang, Fei Liao +3
Tool learning has emerged as a promising paradigm for large language models (LLMs) to address real-world challenges. Due to the extensive and irregularly updated number of tools, t…
Try, Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLMs
Kunfeng Chen, Qihuang Zhong, Juhua Liu +2
Tool-calling empowers Large Language Models (LLMs) to interact with external environments. However, current methods often struggle to handle massive and noisy candidate tools in lo…