20 citations · 93 across the 46 of their papers we have counts for
8 papers · 1 filter
SAGE: Sense-Aligned Multimodal Grounding Evaluation with Gloss-Augmented Reasoning
Sunjae Kwon, Suhaas Kiran DG, Zonghai Yao +3
This paper introduces Sense-Aligned Grounding Evaluation (SAGE), a unified 10-candidate image-selection benchmark that reformulates VWSD, VerSe, and WikiDiverse into a shared term-…
Measure First, Then Allocate: Calibrated Rollout Budgets for MCP Agents in Reinforcement Learning
Hieu Tran, Zonghai Yao, Sunjae Kwon +1
Training tool-using language models requires costly interaction, yet identical rewards within a rollout group provide no group-relative learning signal. We use reward disagreement…
OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction
Mingchen Li, Rohan Pandey, Junhui Qian +5
Opioid overdose remains a major clinical and public health burden, highlighting the need for scalable approaches to identify patients at high risk. Here, we investigate diagnosis-s…
DischargeBench: Pre-Deployment Evaluation of LLM Discharge Educators through Persona-Grounded Patient Simulation
Won Seok Jang, Zonghai Yao, Youngbin Jang +3
Effective discharge education is personalized: a clinician adapts the discharge plan to each patient's literacy, recall, and personality. Before large language models (LLMs) take o…
Medical thinking with multiple images
Zonghai Yao, Benlu Wang, Yifan Zhang +8
Large language models perform well on many medical QA benchmarks, but real clinical reasoning often requires integrating evidence across multiple images rather than interpreting a…
Rethinking Patient Education as Multi-turn Multi-modal Interaction
Zonghai Yao, Zhipeng Tang, Chengtao Lin +5
Most medical multimodal benchmarks focus on static tasks such as image question answering, report generation, and plain-language rewriting. Patient education is more demanding: sys…
Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction
Mingchen Li, Jiatan Huang, Zonghai Yao +1
Large language models (LLMs) hold significant promise for healthcare, yet their reliability in high-stakes clinical settings is often compromised by hallucinations and a lack of gr…
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…