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Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York, 14853, USA
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Achieve high-fidelity, training-free visual generation from coarse inputs by cleverly steering pretrained diffusion models with a noise-adaptive h-transform.
Forget gradients: this new sampler learns complex distributions, even with discrete parameters, by enforcing time-reversibility and comparing forward and backward Markov trajectories.
Forget discrete search and trial-and-error prompting: nabla-Reasoner uses gradient descent in the LLM's latent space to boost reasoning accuracy by 20% while cutting model calls by up to 40%.
Key contribution not extracted.
