Introduces Wasserstein equilibrium decoding that improves accuracy and convergence speed for small VLMs on medical VQA benchmarks by using semantic consensus instead of lexical order.
arXiv preprint arXiv:2504.17119 (2025)
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
TRACE is a taxonomy-grounded synthetic instruction-tuning dataset with 2,999 examples for ABA teaching-program generation and multi-session behavioral interpretation, released with code, provenance, and stratified splits.
TF-IDF with LGBM achieved the highest AUC-ROC of 0.80 and best balance in predicting next-day discharge from clinical notes, outperforming fine-tuned compact LLMs like DistilGPT-2.
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.
citing papers explorer
-
Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Introduces Wasserstein equilibrium decoding that improves accuracy and convergence speed for small VLMs on medical VQA benchmarks by using semantic consensus instead of lexical order.
-
TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis
TRACE is a taxonomy-grounded synthetic instruction-tuning dataset with 2,999 examples for ABA teaching-program generation and multi-session behavioral interpretation, released with code, provenance, and stratified splits.
-
Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes
TF-IDF with LGBM achieved the highest AUC-ROC of 0.80 and best balance in predicting next-day discharge from clinical notes, outperforming fine-tuned compact LLMs like DistilGPT-2.
-
ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook
ECG foundation models for signal interpretation and medical LLMs for reasoning can be integrated into agentic systems for real-time cardiovascular intelligence on edge devices.