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ESS-Flow method enables training-free guidance for generative models

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ESS-Flow, a novel method for guiding pretrained flow-based generative models without requiring retraining. This technique utilizes Bayesian inference in the source space via elliptical slice sampling, leveraging the Gaussian prior inherent in these models. ESS-Flow only needs forward passes through the generative and observation processes, making it suitable for tasks where gradients are unavailable, such as with non-differentiable simulations or quantization. The method has shown promise in scientific applications, including the generation of materials with specific properties, protein structures with enhanced designability, and drug-like molecules. AI

IMPACT Enables more flexible application of generative models in scientific discovery where gradients may be unavailable.

RANK_REASON This is a research paper detailing a new method for generative models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ESS-Flow method enables training-free guidance for generative models

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This is a research paper detailing a new method for generative models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Adhithyan Kalaivanan, Zheng Zhao, Jens Sj\"olund, Fredrik Lindsten ·

    ESS-Flow: Training-free guidance as Bayesian inference in source space

    arXiv:2510.05849v2 Announce Type: replace Abstract: Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on paired data. We present ESS-Flow, a gradient-fre…

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