Agentic artificial intelligence (AI) is moving beyond research demonstrations toward production use at scientific user facilities, including light sources, neutron sources, nanoscience centers, and autonomous laboratories. Its scientific value extends beyond increasing throughput. Agents can perform repeatable tasks in...
Ming Du, Xiang-Yu Yin, Michael H. Prince et al.· 0 citations
This work presents APEXA, a deployed framework coordinating 61 tools for synchrotron data reduction, and introduces APEXA-Bench, 58 facility tasks organized by scientific correctness and physical consequence.
Pawan K. Tripathi, Hemant Sharma, Andrew Chuang et al.· 0 citations
High-Energy Diffraction Microscopy (HEDM) recovers crystallographic orientation, strain, and grain position from rotating-crystal X-ray diffraction patterns. Existing forward models in far-field (FF), near-field (NF), and point-focused (pf) HEDM are not differentiable, which forecloses gradient-based joint parameter re...
Hemant Sharma, Nina Andrejevic, Simon Zhang et al.· 1 citation
This work argues that autonomous scientific systems should emit portable, claim-aware artifact lineage as a minimum audit layer, and proposes a compact observability profile organized around individuals, operators, fitness records, lineage, archives, runs, streams, and steering commands.
Xiang-Yu Yin, Ming Du, Michael H. Prince et al.· 1 citation
Together, the deployed platform and its operations-grounded evaluation present a promising workflow for trustworthy, statistically grounded AI assistance in facility operations, transferable to other large scientific instruments.
Rajat Sainju, Dariusz Jarosz, Hai-Rong Shang et al.· arXiv.org· 0 citations
X-ray fluorescence (XRF) microscopy maps elemental distributions, while optical microscopy can provide complementary morphological context. Localizing XRF fields of view (FOVs) in optical images is difficult because the two modalities differ in contrast mechanism and resolution. Most current workflows place each XRF ti...
Xiang-Yu Yin, T. Paunesku, Letonia Copeland-Hardin et al.· 0 citations
This paper evaluates training-free vision language model (VLM) localization on two datasets representing same-section high-correspondence and adjacent-section low-correspondence imaging and tests unconstrained and metadata-constrained search and VLMs with geometric controls, classical template matching, and two alterna...
Xiang-Yu Yin, T. Paunesku, Letonia Copeland-Hardin et al.· 0 citations
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