BELIEF improves closed-set biomedical QA by converting documents to structured evidence objects and fusing D-S symbolic belief estimation with LLM inference through reliability-aware arbitration.
What disease does this patient have? a large-scale open domain question answering dataset from medical exams,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PPAI proposes prototype-based query-agent scoring and a multi-agent Bayesian game for P2P interoperability among personalized LLM agents on edge devices, claiming up to 7.96% accuracy gain and 16.34% latency reduction.
citing papers explorer
-
BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
BELIEF improves closed-set biomedical QA by converting documents to structured evidence objects and fusing D-S symbolic belief estimation with LLM inference through reliability-aware arbitration.
-
PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
PPAI proposes prototype-based query-agent scoring and a multi-agent Bayesian game for P2P interoperability among personalized LLM agents on edge devices, claiming up to 7.96% accuracy gain and 16.34% latency reduction.