3 papers
cs.AI2026
Answer Probing-Guided Search for Diverse Solution Exploration of LLMs
Yi Fang, Que Shen, Chengpeng Li +6
Generating multiple diverse and high-quality solutions is valuable for many applications, such as code-test generation and drug discovery. However, Large Language Models (LLMs) ten…
cs.IR2026
RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution
Jinming Nian, Fangchen Li, Dae Hoon Park +1
Retrieval algorithms like BM25 and query likelihood with Dirichlet smoothing remain strong and efficient first-stage rankers, yet improvements have mostly relied on parameter tunin…
cs.IR2026
Towards Sample-Efficient and Stable Reinforcement Learning for LLM-based Recommendation
Hongxun Ding, Keqin Bao, Jizhi Zhang +4
While Long Chain-of-Thought (Long CoT) reasoning has shown promise in Large Language Models (LLMs), its adoption for enhancing recommendation quality is growing rapidly. In this wo…