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INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples
Sara Rezaeimanesh, Chad Klochko, Kundan Thind +1
Machine learning models for tabular prediction typically assume that training data are representative of the population on which they will be deployed. In practice, however, data a…
A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
Rafi Ibn Sultan, Chengyin Li, Yiannos Demetriou +6
Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.…
Vision-Language Model Confidence Is Not a Property of the Answer
Reza Khanmohammadi, Erfan Miahi, Ivan Brugere +4
Vision-language models are increasingly deployed behind a confidence gate: the system reads how confident the model is in its answer and defers when confidence is low. This makes t…
Adapt or Calibrate? How Local Labels Buy Certified Coverage in Medical VQA
Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
An answer from a vision-language model (VLM) to a medical image question can be accepted without review only under a threshold bounding the error of accepted answers, and certifyin…
Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur +4
Large vision-language models (LVLMs) suffer from visual ungroundedness: they can produce a fluent, confident, and even correct response driven entirely by language priors, with the…
Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations
Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim +2
Learning-based fluence map prediction offers a fast alternative to iterative inverse planning in intensity-modulated radiation therapy (IMRT), but its robustness under realistic di…
How Reliable are Confidence Estimators for Large Reasoning Models? A Systematic Benchmark on High-Stakes Domains
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur +4
The miscalibration of Large Reasoning Models (LRMs) undermines their reliability in high-stakes domains, necessitating methods to accurately estimate the confidence of their long-f…
What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization
Sara Rezaeimanesh, Kundan Thind, Farzan Siddiqui +1
In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that struggle to generalize to real-worl…