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cs.CL2026
Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models
Soroush Seifi, Vaggelis Dorovatas, Lin Li +2
Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical settings, yet estimating when their answers can be trusted remains underexplored. We systemati…
cs.CL2026
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models
Boqi Chen, Xudong Liu, Jiachuan Peng +5
Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity. We introdu…
cs.CL2025
Energy Landscapes Enable Reliable Abstention in Retrieval-Augmented Large Language Models for Healthcare
Ravi Shankar, Sheng Wong, Lin Li +4
Reliable abstention is critical for retrieval-augmented generation (RAG) systems, particularly in safety-critical domains such as women's health, where incorrect answers can lead t…