Boston schools expand AI learning initiative - Digital Watch Observatory
<a href="https://news.google.com/rss/articles/CBMiekFVX3lxTE1Famo4TFRmZzZadHgyVkhRMndObFJZZnpIWEZDVWc3bWQweThIRHdNSlJTSEFUTVU1dktjY3ZFTTNHTjdiejZyZm9ITlJCNXVKMUs2dldTT0w2NEJQbFh0dXlpTl9xM0puVURaN1VwZEVHX21VRGM3THBB?oc=5" target="_blank">Boston schools expand AI learning initiative</a> <font color="#6f6f6f">Digital Watch Observatory</font>
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arXiv:2603.29492v1 Announce Type: new Abstract: Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically interpretable indicators of when outputs should be thoroughly reviewed, enabling selective radiologist verification and reducing the risk of hallucinated findings influencing clinical decisions. One intuitive approach to this is verbalized confidence, where the model explicitly states its certainty. However, current state-of-the-art language models are often overconfident, and research on calibration in multimodal settings such as radiology report generation is limited. To address this gap, we introduce ConRad (Confidence Calibration for Radiology Reports), a reinforcement learning framework for fin
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