3 papers
cs.AI2026
Agentic Retrieval-Augmented Generation for Financial Document Question Answering
Yang Shu, Yingmin Liu, Zequn Xie
Financial document question answering (QA) demands complex multi-step numerical reasoning over heterogeneous evidence--structured tables, textual narratives, and footnotes--scatter…
cs.CL2026
ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
Chenyu Wang, Yueyuan Li, Yingmin Liu +1
Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice. We present ConflictR…
cs.AI2026
CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models
Yuning Wu, Yingmin Liu, Yang Shu
Large language model (LLM) self-correction -- the ability to detect and fix errors in generated outputs -- remains largely ad hoc, relying on generic prompts such as "please recons…