AI RESEARCH
Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
arXiv CS.CV
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ArXi:2605.18419v1 Announce Type: new Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology. However, fine-tuning billions of parameters on scarce, expert-annotated pathology data is prohibitive, while in-context learning (ICL), which conditions the VLM on nstrative image-text pairs without parameter updates, suffers from high sensitivity to which examples are selected and how the query is phrased, producing unreliable diagnostics.