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A novel proposal-and-verify approach using vision language models can recover accurate localization in challenging low-correspondence imaging scenarios where traditional methods fail.
Group localization of XRF tiles can boost accuracy from zero to nearly perfect by leveraging acquisition geometry, transforming how we integrate multimodal imaging data.
Autonomous scientific agents can now be audited more effectively by linking claims directly to their evidence and verification, transforming how we ensure scientific integrity.
