PPDL: LLM-Based Flows as Probabilistic Programs
arXiv:2608.05234v1 Announce Type: new 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.
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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
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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)
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