3 papers
cs.CY2026
Knowing Isn't Understanding: Re-grounding Generative Proactivity with Epistemic and Behavioral Insight
Kirandeep Kaur, Xingda Lyu, Chirag Shah
Generative AI agents equate understanding with resolving explicit queries, an assumption that confines interaction to what users can articulate. This assumption breaks down when us…
cs.CL2026
P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA
Xingda Lyu, Gongfu Lyu, Zitai Yan +1
Large Language Models (LLMs) demonstrate remarkable capabilities but remain limited by their reliance on static training data. Retrieval-Augmented Generation (RAG) addresses this c…
cs.HC2025
Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy
Lihua Du, Xing Lyu, Lezi Xie +1
AI sycophancy is increasingly recognized as a harmful alignment, but research remains fragmented and underdeveloped at the conceptual level. This article redefines AI sycophancy as…