Naoto Iwase
岩瀬 直人
Medical student
at Nagoya University
About
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 reliable reasoning in large language models. He develops methods for evaluating and improving reasoning, including anytime-valid statistical inference for self-consistency and prefix-consistency evaluation for chain-of-thought reasoning. He also works on medical AI, including benchmarks for detecting and correcting errors in clinical text.
News
- Prefix Consistency and CITE accepted to NeurIPS 2026.
- MedRECT accepted to the ORACLE Workshop at EMNLP 2026.
- New preprints: Prefix-Denoising Consistency and OP²SD.
Publications
Reliable Chain-of-Thought via Prefix Consistency
TL;DR
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×).
CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
TL;DR
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.
MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts
TL;DR
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.
Prefix-Denoising Consistency: Test-Time Verification for Diffusion Language Models
TL;DR
Prefix-Denoising Consistency verifies diffusion language model outputs by regenerating suffixes from a fixed prefix; correct trajectories are more reproducible, improving mathematical and commonsense reasoning under matched compute.
Privileged Solutions or Context-Induced Teacher Behavior? Dissecting On-Policy Self-Distillation
TL;DR
Replacing a problem's paired reference with another problem-solution pair remains competitive with standard on-policy self-distillation, showing that context-induced teacher behavior matters beyond target-specific privileged information.
Awards & Honors
- Excellent Poster Presentation Award, 8th Annual Meeting of the Japanese Association for Medical Artificial Intelligence, June 2026.
- Grade Pre-1 — Pass with Distinction (S), Japan Statistical Society Certificate, January 2025.
- Student Presentation Award (Oral), 38th Annual Meeting of the Japanese Society of Computational Statistics, May 2024.
- Incoming Student Representative and Address, Nagoya University Matriculation Ceremony, April 2021 (top score among admitted applicants to the Medical Program, the university’s most selective undergraduate program).