Naoto Iwase
岩瀬 直人
Naoto Iwase is a medical student at Nagoya University and an incoming visiting student at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He previously worked as a part-time engineer at Preferred Networks, Inc.
His research focuses on making large language models more reliable and useful in medicine. He develops methods and benchmarks for reliable LLM reasoning, including anytime-valid statistical inference for self-consistency, prefix-consistency evaluation for chain-of-thought reasoning, and clinical text error correction.
He also writes ML Notes, a collection of survey-style notes on recent machine learning papers.
Publications
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Reliable Chain-of-Thought via Prefix Consistency
Correct chain-of-thought traces reproduce their answer under prefix regeneration more often than wrong ones; weighting majority voting by this prefix consistency reaches plateau accuracy at up to 21× fewer tokens (median 4.6×).
[paper] [project page] [code]
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CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
CITE certifies a prespecified answer as the unique mode under arbitrary data-dependent stopping, while controlling false certification without knowing the response category set in advance.
[paper]
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MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts
A bilingual (Japanese/English) benchmark for medical error correction built from licensing exams; across 9 LLMs, reasoning models substantially outperform standard architectures, and a fine-tuned model exceeds human expert performance.
Awards & Honors
- Excellent Poster Presentation Award, 8th Annual Meeting of the Japanese Association for Medical Artificial Intelligence, June 2026.
- Outstanding Performance Award (S grade), Japan Statistical Society Certificate, Pre-1st Grade, January 2025.
- Student Presentation Award (Oral), 38th Annual Meeting of the Japanese Society of Computational Statistics, May 2024.
- Highest Score in the Entrance Examination, Nagoya University School of Medicine, 2021.