3 papers
cs.AI2026
RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
Yilun Hao, Krishna Sayana, Isabella Ye +4
Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed simil…
cs.IR2026
AgenticTagger: Structured Item Representation for Recommendation with LLM Agents
Zhouhang Xie, Bo Peng, Zhankui He +11
High-quality representations are a core requirement for effective recommendation. In this work, we study the problem of LLM-based descriptor generation, i.e., keyphrase-like natura…
cs.NE2026
PACEvolve: Enabling Progress-Aware Consistent Evolution
Minghao Yan, Bo Peng, Benjamin Coleman +13
Self-evolving agents powered by Large Language Models (LLMs) have emerged as a promising direction across diverse domains, including code optimization and scientific discovery, yet…