{"slug": "search-at-the-cost-of-sampling-nearly-instant-latent-space-bayesian-optimization", "title": "Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization", "summary": "A new arXiv paper (2609.19476v1) reports a latent space Bayesian optimization method that achieves at least a 100x speedup over state-of-the-art baselines while matching or improving performance on molecular and image generation benchmarks. The authors derive nearly closed-form solutions to the surrogate modelling and acquisition problems by exploiting a linear model constrained to a spherical domain where high-dimensional latents concentrate, building on recent work justifying linear surrogates. The method is positioned as a practical drop-in for de novo discovery pipelines where sequential Bayesian optimization was previously too slow to consider.", "body_md": "arXiv:2609.19476v1 Announce Type: new \nAbstract: Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of candidates to experimentally validate. While Bayesian optimization (BO) is a natural fit for this setting, as it uses past evaluations to guide future proposals, the computational overhead required for its sequential decision-making becomes a bottleneck when virtual screens are relatively cheap. We make BO practical in this regime by exploiting the unique combination of a linear model constrained to a spherical domain where high-dimensional latents concentrate. We build off recent work justifying the use of linear surrogates, while deriving nearly closed-form solutions to the surrogate modelling and acquisition problems that exploit spherical symmetry. The result is at least a 100x speedup over state-of-the art baselines, with matching or improved performance across molecular and image generation benchmarks. Altogether, our method makes BO a practical drop-in for de novo pipelines where it was previously too slow to consider.", "url": "https://wpnews.pro/news/search-at-the-cost-of-sampling-nearly-instant-latent-space-bayesian-optimization", "canonical_source": "https://arxiv.org/abs/2609.19476", "published_at": "2026-09-18 04:00:00+00:00", "updated_at": "2026-09-18 04:23:50.557142+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "generative-ai", "ai-tools"], "entities": ["arXiv", "Bayesian optimization"], "alternates": {"html": "https://wpnews.pro/news/search-at-the-cost-of-sampling-nearly-instant-latent-space-bayesian-optimization", "markdown": "https://wpnews.pro/news/search-at-the-cost-of-sampling-nearly-instant-latent-space-bayesian-optimization.md", "text": "https://wpnews.pro/news/search-at-the-cost-of-sampling-nearly-instant-latent-space-bayesian-optimization.txt", "jsonld": "https://wpnews.pro/news/search-at-the-cost-of-sampling-nearly-instant-latent-space-bayesian-optimization.jsonld"}}