14 papers
Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
Yu Xia, Zhouhang Xie, Xin Xu +4
Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control. Exist…
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
Minghao Yan, Bo Peng, Benjamin Coleman +11
Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in prac…
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…
Evaluation on Entity Matching in Recommender Systems
Zihan Huang, Rohan Surana, Zhouhang Xie +3
Entity matching is a crucial component in various recommender systems, including conversational recommender systems (CRS) and knowledge-based recommender systems. However, the lack…
PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution
Minghao Yan, Bo Peng, Benjamin Coleman +13
Large Language Models (LLMs) have emerged as powerful operators for evolutionary search, yet the design of efficient search scaffolds remains ad hoc. While promising, current LLM-i…
GUI Agents: A Survey
Dang Nguyen, Jian Chen, Yu Wang +27
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomo…