翻訳待ち：PPDL: LLM-Based Flows as Probabilistic Programs

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--> [Submitted on 5 Aug 2026] Title:PPDL: LLM-Based Flows as Probabilistic Programs View a PDF of the paper titled PPDL: LLM-Based Flows as Probabilistic Programs, by Louis Mandel and 3 other authors View PDF Abstract:Building reliable applications that leverage large language models (LLMs) remains a significant challenge. While LLMs offer impressive capabilities across diverse tasks, their outputs often lack accuracy and provide no clear measure of confidence. This uncertainty compounds in flows of multiple calls to LLMs and other tools, making it difficult for developers and end-users to trust the results. This paper introduces a probabilistic language for programming LLM-based flows. It enables developers to quantify and propagate uncertainty throughout the application's flow, and experiment with different inference scaling techniques without adding a single line of code beyond the flow's logic. We present an experimental study to demonstrate this capability, and a case study building a theorem proving agent for the Rocq theorem prover. Comments: Published at ICML 2026 Subjects: Machine Learning (cs.LG); Programming Languages (cs.PL) Cite as: arXiv:2608.05234 [cs.LG] (or arXiv:2608.05234v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.05234 arXiv-issued DOI via DataCite (pending registration) Submission history From: Guillaume Baudart [view email] [v1] Wed, 5 Aug 2026 13:37:25 UTC (115 KB) Full-text links: Access Paper: View a PDF of the paper titled PPDL: LLM-Based Flows as Probabilistic Programs, by Louis Mandel and 3 other authors View PDF TeX Source view license Current browse context: cs.LG new | recent | 2026-08 Change to browse by: cs cs.PL References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both 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. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)