3 papers
cs.CL2026
SE-Search: Self-Evolving Search Agent via Memory and Dense Reward
Jian Li, Yizhang Jin, Dongqi Liu +9
Retrieval augmented generation (RAG) reduces hallucinations and factual errors in large language models (LLMs) by conditioning generation on retrieved external knowledge. Recent se…
cs.CL2026
Disco-RAG: Discourse-Aware Retrieval-Augmented Generation
Dongqi Liu, Hang Ding, Qiming Feng +6
Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most exis…
cs.CL2025
LLM-Oriented Token-Adaptive Knowledge Distillation
Xurong Xie, Zhucun Xue, Jiafu Wu +5
Knowledge distillation (KD) is a key technique for compressing large-scale language models (LLMs), yet prevailing logit-based methods typically employ static strategies that are mi…