4 papers · 1 filter
MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents
Haozhen Zhang, Quanyu Long, Jianzhu Bao +4
Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors ab…
Self-Verification Dilemma: Experience-Driven Suppression of Overused Checking in LLM Reasoning
Quanyu Long, Kai Jie Jiang, Jianda Chen +3
Large Reasoning Models (LRMs) achieve strong performance by generating long reasoning traces with reflection. Through a large-scale empirical analysis, we find that a substantial f…
Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive Queries
Yin Wu, Quanyu Long, Jing Li +2
Retrieval-augmented generation (RAG) is a paradigm that augments large language models (LLMs) with external knowledge to tackle knowledge-intensive question answering. While severa…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…