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Analytic bridge diffusions offer controlled path generation without neural networks

Researchers have developed a new method called Analytic Bridge Diffusions (LQ-GM-PID) that can generate controlled paths without relying on neural networks for optimization. This approach analytically solves a restricted class of transport problems, providing closed-form solutions for score, intermediate marginals, and protocol gradients. The method is demonstrated to be efficient, achieving sub-50ms precomputation on a laptop for complex tasks, and serves as a benchmark for current neural bridge-diffusion techniques. AI

影响 Provides an analytically solvable reference model for testing neural approximations in generative transport methods.

排序理由 This is a research paper published on arXiv detailing a new analytical method for controlled path generation. [lever_c_demoted from research: ic=1 ai=1.0]

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Analytic bridge diffusions offer controlled path generation without neural networks

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  1. arXiv cs.LG TIER_1 English(EN) · Michael Chertkov ·

    Analytic Bridge Diffusions for Controlled Path Generation

    arXiv:2605.02961v1 Announce Type: new Abstract: Most modern bridge-diffusion methods achieve finite-time transport by specifying an interpolation, Schr\"odinger-bridge, or stochastic-control objective and then learning the associated score or drift field with a neural network. In…