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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2026 2verdicts
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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.
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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.
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Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
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.