Probabilistic Programming

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0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Induction and Inquiry via Probabilistic Reasoning over Language and Code
Sep 1, 2026Wasu Top Piriyakulkij, Sam Acquaviva, Cassidy Langenfeld +2Cognitive Modeling Active Learning
1Cornell University · 2Massachusetts Institute of Technology
Foundations of MT-PDCL: Measure-Theoretic Probabilistic Definite Clause Logic
Aug 13, 2026Costin Bădică, Amelia BădicăProbabilistic Logic Logic Programming
Department of Computers and Information Technology University of Craiova Craiova, Romania · Department of Business Informatics University of Craiova Craiova, Romania
LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs
Aug 9, 2026Maria-Nicoleta Crăciun, C. -H. Luke Ong, Tom Schrijvers +1Markov Chain Monte Carlo Automatic Differentiation
University of Oxford, UK · Nanyang Technological University, Singapore, Singapore · KU Leuven, Leuven, Belgium
From probability to causality in probabilistic logic programming
Aug 7, 2026Zora Wurm, Kilian Rückschloß, Felix WeitkämperProbabilistic Logic Causal Structure Learning
Ludwig-Maximilians-Universität München, Oettingenstraße 67, 80538 München · Eberhard-Karls-Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen · German University of Digital Science, Marlene-Dietrich-Allee 14, 14482 Potsdam
PPDL: LLM-Based Flows as Probabilistic Programs
Aug 5, 2026Louis Mandel, Guillaume Baudart, Mandana Vaziri +1LLM Inference LLM Reliability
IBM, New York, USA · Université Paris Cité, Inria, CNRS, IRIF, France
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Jul 23, 2026Kilian Rueckschloss, Felix WeitkaemperProbabilistic Logic Logic Programming
Eberhard Karls Universitaet Tuebingen · University of Tübingen Tübingen, Germany · German University of Digital Science +1
GradInf: Gradient Estimation as Probabilistic Inference
Jul 8, 2026Gaurav Arya, Mathieu Huot, Moritz Schauer +2Probabilistic Programming
Carnegie Mellon University, Pittsburgh, USA · Massachusetts Institute of Technology, Cambridge, USA · Chalmers University of Technology & University of Gothenburg, Gothenburg, Sweden +1
Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models
Jun 30, 2026Jian Xu, Delu Zeng, John Paisley +1Automated Program Repair Bayesian Inference
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1
The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models
Jun 29, 2026Rafael Kaufmann, Felix Neubürger, Michael Walters +2Neuro-Symbolic AI Probabilistic Programming
GAIA Lab. · Primordia Co.. · South Westphalia University of Applied Sciences, Lindenstr 53, Meschede, 59872, Germany. +1
DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs
Jun 18, 2026Saimun Habib, Vaishak Belle, Fengxiang HeProbabilistic Logic Structural Causal Models
University of Edinburgh
NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning
Jun 17, 2026Daniel Romero Schellhorn, Till Mossakowski, Björn GehrkeNeuro-Symbolic Reasoning Neurosymbolic Learning
University of Osnabrück, Osnabrück, Germany
Dynestyx: A Probabilistic Programming Library for Dynamical Systems
Jun 15, 2026Daniel Waxman, Dmitry Batenkov, John Feser +4Dynamical Systems Parameter Estimation
1Basis Research Institute · 2Massachusetts Institute of Technology
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
May 26, 2026Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake +1Language Model Calibration LLM Uncertainty Estimation
Department of Computer Science Princeton University · Princeton AI Lab Princeton University · Department of Psychology and Computer Science Princeton University
AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
May 18, 2026Jungang Zou, Alex Ziyu Jiang, Qixuan ChenMarkov Chain Monte Carlo Bayesian Inference
Department of Biostatistics, Columbia University
Probabilistic Programs of Thought
Apr 19, 2026Poorva Garg, Renato Lui Geh, Daniel Israel +3LLM-Based Program Synthesis Efficient Language Model Reasoning
University of California Los Angeles · Allen Institute for AI

Sep 1, 2026, cs.AIJ/K move · Enter open · S save

Induction and Inquiry via Probabilistic Reasoning over Language and Code

Wasu Top Piriyakulkij, Sam Acquaviva, Cassidy Langenfeld, Joshua Tenenbaum+1

1Cornell University · 2Massachusetts Institute of Technology

Cognitive Modeling Active Learning Probabilistic Programming

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How humans grow and maintain abstract knowledge from the sparse, streaming noisy data of experience is a longstanding challenge in cognitive science. Any computational account must satisfy at least three desiderata: It must be (1) data-efficient and compute-efficient, (2) capture gradations of uncertainty to support intelligent inquiry and information gathering, and (3) be flexible enough to mentally represent the endless range of concepts people can learn and think about. Here we introduce a computational model that captures these three properties, by encoding symbolic knowledge as mental programs that combine natural language with source code, and sequentially inferring mental programs using LLM-guided Bayesian learning algorithms. Across a range of behavioral studies this model successfully reproduces quantitative signatures of human inductive learning and active inquiry, such as anchoring, garden-pathing, and other effects. In contrast, pure LLMs and classic Bayesian models either fail at the underlying task, or do not reproduce human behavior, or succeed only at exorbitant computational cost. These results suggest that one way humans continually grow their knowledge is by mentally representing many hypotheses spanning language-like and program-like representations, then revising those hypotheses to approximate Bayesian updates, while a bottom-up neural mechanism (an LLM) makes inference both tractable and learnable.

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