A semi-autonomous multi-agent AI workflow, aided by humans, won first place in a cosmological parameter inference challenge using efficient neural networks and calibration techniques.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
ORACLE-2 multimodal classifiers raise macro F1 from 0.52-0.66 (light-curve only) to 0.73 on ZTF Bright Transient Survey data and reach 0.88 on simulated ELAsTiCC data.
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Competing with AI Scientists: Agent-Driven Approach to Astrophysics Research
A semi-autonomous multi-agent AI workflow, aided by humans, won first place in a cosmological parameter inference challenge using efficient neural networks and calibration techniques.