3 citations · 3 across the 5 of their papers we have counts for
6 papers
ChainRec: An Agentic Recommender Learning to Route Tool Chains for Diverse and Evolving Interests
Fuchun Li, Qian Li, Xingyu Gao +7
Large language models (LLMs) are increasingly integrated into recommender systems, motivating recent interest in agentic and reasoning-based recommendation. However, most existing…
What Makes an Ideal Quote? Recommending "Unexpected yet Rational" Quotations via Novelty
Bowei Zhang, Jin Xiao, Guanglei Yue +4
Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignor…
ChemAmp: Amplified Chemistry Tools via Composable Agents
Zhucong Li, Powei Chang, Jin Xiao +6
Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool con…
ChemHTS: Hierarchical Tool Stacking for Enhancing Chemical Agents
Zhucong Li, Jin Xiao, Bowei Zhang +5
Large Language Models (LLMs) have demonstrated remarkable potential in scientific research, particularly in chemistry-related tasks such as molecular design, reaction prediction, a…
QUILL: Quotation Generation Enhancement of Large Language Models
Jin Xiao, Bowei Zhang, Qianyu He +6
While Large language models (LLMs) have become excellent writing assistants, they still struggle with quotation generation. This is because they either hallucinate when providing f…
Can Large Language Models Understand Real-World Complex Instructions?
Qianyu He, Jie Zeng, Wenhao Huang +14
Large language models (LLMs) can understand human instructions, showing their potential for pragmatic applications beyond traditional NLP tasks. However, they still struggle with c…