3 papers
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…
cs.AI2026
Controllable LLM Reasoning via Sparse Autoencoder-Based Steering
Yi Fang, Wenjie Wang, Mingfeng Xue +4
Large Reasoning Models (LRMs) exhibit human-like cognitive reasoning strategies (\eg backtracking, cross-verification) during the reasoning process, which improves their performanc…