3 papers
cs.CL2026
Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty
Sravanthi Machcha, Sushrita Yerra, Sahil Gupta +4
Current evaluation of large language models (LLMs) overwhelmingly prioritizes accuracy; however, in real-world and safety-critical applications, the ability to abstain when uncerta…
cs.AI2025
T-CPDL: A Temporal Causal Probabilistic Description Logic for Developing Logic-RAG Agent
Hong Qing Yu
Large language models excel at generating fluent text but frequently struggle with structured reasoning involving temporal constraints, causal relationships, and probabilistic reas…
cs.CL2025
RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration
Hong Qing Yu, Frank McQuade
This paper presents RAG-KG-IL, a novel multi-agent hybrid framework designed to enhance the reasoning capabilities of Large Language Models (LLMs) by integrating Retrieval-Augmente…