Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
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2 Pith papers cite this work, alongside 2,900 external citations. Polarity classification is still indexing.
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Pith papers citing it
2,900
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UNVERDICTED 2representative citing papers
ResVLA anchors generative VLA policies on low-frequency intent predictions and refines high-frequency residuals via diffusion bridges, yielding competitive performance and faster convergence in simulation and real-robot tests.
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Optimal scenario design for climate emulation
Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
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From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
ResVLA anchors generative VLA policies on low-frequency intent predictions and refines high-frequency residuals via diffusion bridges, yielding competitive performance and faster convergence in simulation and real-robot tests.