Researchers present a single vision-language model designed to handle three prostate cancer imaging tasks at once: generating PET/CT reports, answering visual questions, and segmenting lesions. The work argues that prior PET/CT AI systems typically tackle these tasks in isolation, and that combining them may improve clinical usefulness.
A new arXiv paper introduces TestHallVQA, a benchmark built from scientific exam material for evaluating large vision-language models on visual question answering over long, multi-page documents. The authors argue that current planar VQA benchmarks tend to test isolated skills rather than document-level reasoning amid redundant context. The benchmark is intended to expose where such models fail when relevant information is buried in lengthy inputs.
Researchers introduce NoteVQA, a benchmark that evaluates vision-language models on visual questions drawn from real human communities rather than pre-defined task categories. The work argues that current benchmarks focus on narrow capabilities such as multi-hop retrieval and miss the variety of questions users ask in daily life, including consumer AI search. It is published as an arXiv preprint.