Generative models can’t discover what they can’t reach. We’re excited to introduce ActFlow: a continued pre-training scheme that actively expands the valid design space reachable by flow and diffusion models. We call this generable set expansion — a new learning principle for out-of-distribution generative modeling, and a step toward evolvable search spaces for scientific discovery. (1/5)

I’m at #ICML2026 presenting several works on flow and diffusion models for discovery: from theory and algorithms to molecules, peptides, and proteins. These works have been done with amazing students and collaborators at @ETH_AI_Center, Caltech, KTH, and UPenn, including @maltefranke_ , @KimonProto6689, @yapinghsieh, @cristianpjensen, Sven Gutjahr, Luca Schaufelberger, Zifan Wang, @_sophia_tang_, Bruce Lee, Chenghao Liu, @kjelljorner, Karl Henrik Johansson, @pranamanam, @yisongyue, and @arkrause. Feel free to reach out if you’d like to chat these days at ICML!

How can we perform generative (scientific) discovery beyond the data? Excited to present our paper “Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning” as ✨Spotlight✨ this Wednesday at NeurIPS 2025. We employ calculus of variations to strictly generalize RL-based fine-tuning schemes. This unlocks new fundamental capabilities for generative discovery, including generative exploration: the ability to sample from low-probability, yet promising regions, even beyond the original training data. Feel free to reach out if you’re interested in generative discovery (methods, theory, chem/bio applications) — and check out riccardodesanti.com for updates. We plan to release soon a generative exploration library! 📄 Paper: arxiv.org/abs/2511.22640 ⏳Wednesday at 1pm (San Diego time), Exhibit Hall C,D,E #3619 Work done with amazing collaborators @vlastelicap , @yapinghsieh, @ZebangShen, Niao He, and @arkrause .

Generative models are great at mimicking data — but real (scientific) discovery requires going beyond it. Excited to present our paper “Provable Maximum Entropy Manifold Exploration via Diffusion Models” this Wednesday at ICML 2025! We propose a scalable, theoretically grounded method to fine-tune a pre-trained diffusion model to become maximally explorative over its learned manifold. This makes it possible to go beyond high-density regions and uncover hidden modes via a novel mechanism for self-guided surprise maximization. Feel free to reach out if interested — and check out riccardodesanti.com for updates! 📄 Paper: arxiv.org/abs/2506.15385 ⏳Wednesday at 4:30pm (Vancouver time), Hall A-B/E-2011! Work done with amazing collaborators @vlastelicap, @yapinghsieh, @ZebangShen, Niao He, and @arkrause

Over the last years we understood how to control generative models via RL/control to maximize expected rewards under KL regularization. But for many applications, that’s not enough. Hence the question: can we control them further? Excited to present our ✨Oral✨ paper “Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning” this Friday at the Generative AI and Biology Workshop at ICML 2025! We propose a theoretically grounded method to fine-tune a pre-trained diffusion or flow model for arbitrary objective functions — even beyond expected reward. For instance, this is crucial in scientific discovery, where the goal is often to maximize the quality of the single best sample rather than the average sample quality. We achieve this by bridging probability-space optimization and flow modeling enabling general optimization processes over generative models via sequential fine-tuning - with guarantees. Feel free to reach out if you’re interested — and check out riccardodesanti.com for updates! 📄 Paper: shorturl.at/I1wNr ⏳Friday at 2:35pm (Vancouver time), BioGen Workshop at ICML 2025 Work done with amazing collaborators @vlastelicap, @yapinghsieh, @ZebangShen, Niao He, and @arkrause