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20242026
most citedSmall Language Models Can Use Nuanced Reasoning For Health Science Research Classification: A Microbial-Oncogenesis Case Study

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

q-bio.QM2026

Artificial Intelligence Can Match Domain Experts in Evidence Extraction and Critical Appraisal of Microbial Oncogenesis Research Publications

Kaela Kokkas, Hairong Wang, Richard Klein +11

Confirmed oncogenic microbes contribute significantly to cancer burden. Identifying novel microbial oncogenicity could yield strategies that will reduce disease burdens. However, r…

cs.AI2026

Interoceptive Attention as Dynamic Homeostatic Prioritization in a Foraging Agent

St John Grimbly, Nicolas Kuske, Evert A. Boonstra +7

Biological systems must regulate competing needs under limited perceptual bandwidth, where sharpening one estimate costs the capacity to sharpen the others. Any fixed-budget system…

cs.LG2026

Can LLMs Accurately Score Medical Diagnoses and Clinical Reasoning?

Amy Rouillard, Sitwala Mundia, Linda Camara +8

Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators. Here, we evaluate an…

cs.LG2026

Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models

Bruce A. Bassett, Amy Rouillard, Sitwala Mundia +8

Background: Large language models (LLMs) are increasingly proposed for diagnostic support, but few evaluations use real-world multimodal inpatient data, particularly in low and mid…

cs.CV2026

From Handwriting to Structured Data: Benchmarking AI Digitisation of Handwritten Forms

Nicholas Pather, Joshua Fouché, Sitwala Mundia +5

Manual digitisation of structured handwritten documents is slow and costly. We benchmark 17 leading frontier multi-modal large language models and open-source models against a very…

cs.CL2026

Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLM

Everlyn Asiko Chimoto, Mostafa Elhoushi, Bruce A. Bassett

Quantization is an effective technique for reducing the storage footprint and computational costs of Large Language Models (LLMs), but it often results in performance degradation.…