activity
20242026
collaborators

7 papers

cs.CR2026

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…

cs.CL2026

Confidence Estimation for Financial Vision-Language Models in Chart and Document Understanding

Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley +2

LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritative-looking answer is sometime…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.AI2025

Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agent

Humza Nusrat, Luke Francisco, Bing Luo +10

Stereotactic radiosurgery (SRS) demands precise dose shaping around critical structures, yet black-box AI systems have limited clinical adoption due to opacity concerns. We tested…

cs.CL2024

Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction

Reza Khanmohammadi, Ahmed I. Ghanem, Kyle Verdecchia +9

Large Language Models (LLMs) offer significant potential for clinical symptom extraction, but their deployment in healthcare settings is constrained by privacy concerns, computatio…