{"slug": "trees-to-flows-and-back-unifying-decision-trees-and-diffusion-models", "title": "Trees to Flows and Back: Unifying Decision Trees and Diffusion Models", "summary": "Researchers have established a mathematical correspondence between hierarchical decision trees and diffusion processes, unifying the two model classes under a shared optimization principle called Global Trajectory Score Matching. The work yields two practical applications: TreeFlow, which achieves competitive tabular data generation with higher fidelity and 2× speedup, and DSMTree, a distillation method that transfers decision logic into neural networks within 2% of teacher performance. The unification reveals gradient boosting as asymptotically optimal for the shared framework, bridging discrete and continuous machine learning approaches.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 1 May 2026 (\n\n[v1](https://arxiv.org/abs/2605.00414v1)), last revised 21 May 2026 (this version, v2)]# Title:Trees to Flows and Back: Unifying Decision Trees and Diffusion Models\n\n[View PDF](/pdf/2605.00414)\n\nAbstract:Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp mathematical correspondence between hierarchical decision trees and diffusion processes in appropriate limiting regimes. Our unification reveals a shared optimization principle: \\emph{Global Trajectory Score Matching (GTSM)}, for which gradient boosting (in an idealized version) is asymptotically optimal. We underscore the conceptual value of our work through two key practical instantiations: \\treeflow, which achieves competitive generation quality on tabular data with higher fidelity and a 2\\times computational speedup, and \\dsmtree, a novel distillation method that transfers hierarchical decision logic into neural networks, matching teacher performance within 2\\% on many benchmarks.\n\n## Submission history\n\nFrom: Sai Niranjan Ramachandran [[view email](/show-email/4bd0d387/2605.00414)]\n\n**Fri, 1 May 2026 05:19:54 UTC (8,277 KB)**\n\n[[v1]](/abs/2605.00414v1)**[v2]** Thu, 21 May 2026 04:49:57 UTC (8,277 KB)\n\n### Current browse context:\n\ncs.LG\n\nChange to browse by:\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/trees-to-flows-and-back-unifying-decision-trees-and-diffusion-models", "canonical_source": "https://arxiv.org/abs/2605.00414", "published_at": "2026-06-06 12:59:59+00:00", "updated_at": "2026-06-06 13:17:30.855417+00:00", "lang": "en", "topics": ["machine-learning", "generative-ai", "neural-networks", "artificial-intelligence", "ai-research"], "entities": ["Sai Niranjan Ramachandran"], "alternates": {"html": "https://wpnews.pro/news/trees-to-flows-and-back-unifying-decision-trees-and-diffusion-models", "markdown": "https://wpnews.pro/news/trees-to-flows-and-back-unifying-decision-trees-and-diffusion-models.md", "text": "https://wpnews.pro/news/trees-to-flows-and-back-unifying-decision-trees-and-diffusion-models.txt", "jsonld": "https://wpnews.pro/news/trees-to-flows-and-back-unifying-decision-trees-and-diffusion-models.jsonld"}}