Papers by Yinuo Wang

2 papers
ConMA : Confidence-Guided Kernel Sampling with Multi-Stage Aggregation for LLM Reasoning (2026.findings-acl)

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Challenge: Existing approaches to test-time scaling rely on external verifiers and one-shot independent sampling.
Approach: They propose a test-time scaling framework that reallocates a fixed inference budget into iterative sample–filter–diversify–select cycles.
Outcome: ConMA outperforms baselines on multiple benchmarks while converging early with only 18 samples on average, substantially reducing inference cost.
Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025.emnlp-main)

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Challenge: achieving comprehensive trustworthiness in medical QA poses significant challenges due to complexity of healthcare data, critical nature of clinical scenarios, and multifaceted dimensions of trustworthy AI.
Approach: They examine six key dimensions of trustworthiness in medical QA . they compare how each dimension is evaluated in existing LLM-based systems .
Outcome: The findings show that large language models have improved patient safety and effectiveness . the models exhibit critical trust failures when deployed in clinical settings .

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