# Medical Question Answering

Small language models for clinical decision support often answer at far greater length than the question requires after fine-tuning, and existing length control relies on preference data, reward models or proprietary teachers. PECA adds a differentiable conciseness term to the QLoRA supervised fine-tuning objective: once generation passes a token budget, it raises the end-of-sequence probability, with no preference data, no auxiliary model and a single commodity GPU. On the 1.7-billion-parameter Qwen3-1.7B-Instruct, PECA reduces mean response length from 256.0 to 69.7 tokens, a 72.8% cut, while raising MedMCQA multiple-choice diagnostic accuracy from 40.8% to 44.0%.

Small language models for clinical decision support often answer at far greater length than the question requires after fine-tuning, and existing length control relies on preference data, reward models or proprietary teachers. PECA adds a differentiable conciseness term to the QLoRA supervised fine-tuning objective: once generation passes a token budget, it raises the end-of-sequence probability, with no preference data, no auxiliary model and a single commodity GPU. On the 1.7-billion-parameter Qwen3-1.7B-Instruct, PECA reduces mean response length from 256.0 to 69.7 tokens, a 72.8% cut, while raising MedMCQA multiple-choice diagnostic accuracy from 40.8% to 44.0%.

Chest X-ray report generation moves to question-answer output: predicted disease labels align image and text representations, a medical knowledge graph supplies retrieved evidence, and a fine-tuned LLM rewrites the result, outperforming prior methods on most metrics on MIMIC-CXR and IU X-Ray.

Chest X-ray report generation moves to question-answer output: predicted disease labels align image and text representations, a medical knowledge graph supplies retrieved evidence, and a fine-tuned LLM rewrites the result, outperforming prior methods on most metrics on MIMIC-CXR and IU X-Ray.

The work introduces ProbeGuard, a certified abstention framework that decides from how multi-round consensus formed rather than from terminal agreement: it derives trajectory features from the system's execution log, checks unanimous first-round votes with rationale semantic entropy and an active counter-evidence probe, and converts these scores into a distribution-free selective-risk bound via stratified Learn-then-Test calibration; on MedQA terminal agreement scores 0.512 AUROC while the process score reaches 0.696, and 13.4% of unanimous votes are wrong, with the certified rule answering 55.3% of that layer at 9.0% observed risk.

The work introduces ProbeGuard, a certified abstention framework that decides from how multi-round consensus formed rather than from terminal agreement: it derives trajectory features from the system's execution log, checks unanimous first-round votes with rationale semantic entropy and an active counter-evidence probe, and converts these scores into a distribution-free selective-risk bound via stratified Learn-then-Test calibration; on MedQA terminal agreement scores 0.512 AUROC while the process score reaches 0.696, and 13.4% of unanimous votes are wrong, with the certified rule answering 55.3% of that layer at 9.0% observed risk.