M. Mankowski

Preprint Oct 2026

Large language models are vulnerable to incidental information in clinical documentation and reasoning

Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by assessing their sensitivity to information incidental to the patient encounter. In 576 patient-clinician dialogues, we...

K. Vishwanath, Brandon Ye, A. Alyakin et al. · 0 citations

#natural language process... Preprint Oct 2026

Incidental information contaminates patient notes and disrupts clinical reasoning in large language models

Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by assessing their sensitivity to information incidental to the patient encounter. In 576 patient-clinician dialogues, we...

K. Vishwanath, Brandon Ye, A. Alyakin et al. · 0 citations

#artificial intelligence Jul 2026

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training

Bounded unfiltered teacher continuations at learner-induced contexts improve over pure behavioral cloning at matched budgets and suggest that a few teacher steps, placed at learner-induced contexts, can be a more cost-efficient supervision allocation than longer or more heavily curated teacher completions.

Junze Ye, Jiayi Cheng, Miao Lu et al. · arXiv.org · 2 citations