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Pentabrid 27B — Medical Reasoning Models

Models supporting the Nature Medicine Matters Arising response to Oermann and Vishwanath (2026), "General-purpose large language models outperform specialized clinical AI tools on medical benchmarks" (s41591-026-04431-5).

Core finding

At fixed scale (27B) and offline (no retrieval), the fine-tuning procedure is a first-order determinant of medical reasoning accuracy. A short chain-of-thought fine-tune collapsed the base model's reasoning length and dropped MedXpertQA accuracy by 16.9 points. A corrected long chain-of-thought approach restored reasoning length and recovered accuracy to near parity with the base model. The claim is recovery to near-parity, reversing the regression, not superiority over the base model.

Contents of this repository

Folder	Model	Manuscript label	MedXpertQA (2,450)	Median reasoning tokens
V14_27B_merged	Long chain-of-thought (primary)	Fine-tune B	41.7%	3,700
V15_lora	50/50 data-mix ablation (adapter)	—	41.4%	3,738
V16_a64_lora	LoRA alpha=64 ablation (adapter)	—	42.2%	~4,000

For reference, the untuned base (Qwen3.6-27B) scores 43.8% (3,100 tokens), and the short chain-of-thought fine-tune (Fine-tune A / V13) scores 26.9% (~760 tokens). V15 and V16 are two independent null ablations confirming the recovery is robust to configuration.

Manuscript-to-repository mapping: Fine-tune A corresponds to V13 and Fine-tune B corresponds to V14.

Usage

V14_27B_merged is a full merged model. The V15 and V16 folders are LoRA adapters and must be applied to the base model Qwen3.6-27B.

Reproducibility

Evaluation scripts, the exact scoring script, and the full results table are in the companion dataset repository: https://huggingface.co/datasets/Clinical-Reasoning-Hub/pentabrid-reproducibility

Authors and license

Dr. Adnan Agha (ORCID 0000-0002-2704-8931) and Dr. Eram Anwar (ORCID 0009-0006-9335-9208), UAEU College of Medicine and Health Sciences / Tawam Hospital. IP: UAEU Application #2442. License: CC-BY-NC-ND-4.0.

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