Feynman-Kac

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Unifying Generative Models with Path Integrals
Aug 12, 2026Ramon WinterhalderFeynman-Kac Generative Models
Path Integral Value Matching for Linear Quadratic Stochastic Optimal Control
Aug 11, 2026Bangyan Liao, Chenglei Yu, Yuchen Yang +4Stochastic Optimal Control Feynman-Kac
Neural solutions of coupled ghost and gluon Dyson--Schwinger equations in Landau gauge
Jul 23, 2026Rodrigo Carmo TerinEffective Field Theory Feynman-Kac
A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs
Jul 23, 2026Bizu Feng, Zhimu Yang, Shuming Wang +4Graph Neural Networks Shapley Value
Analytic Distribution of Classifier-Free Guidance for Schedule Design
Jul 22, 2026Enze Jiang, Zheng MaNeural Guidance Feynman-Kac
Nuclear Quantum Effects as a Denoising Problem
Jul 22, 2026Weizhou Wang, Jonathan Weare, Aaron R. DinnerBoltzmann Distributions Feynman-Kac
DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery
Jul 9, 2026Fuling Chen, Kevin Vinsen, Phillip Melton +1Symbolic Regression Feynman-Kac
FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation
Jul 6, 2026Weichen Qin, Yufan Xie, Peihao Wang +8Posterior Sampling Posterior
Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling
Jul 6, 2026Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2Diffusion Sampling Monte Carlo
Language models guide symbolic equation discovery by controlling search
Jul 5, 2026Zikai Xie, Wenmei Li, Man Luo +2Symbolic Regression Hypothesis Generation
A Mathematical Introduction to Diffusion Models
Jul 2, 2026Jianfeng LuDiffusion Sampling Feynman-Kac
Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search
Jul 1, 2026Binglin Ji, Anindya Sarkar, Hengchang Lu +2Training-Free Reward Alignment Feynman-Kac
Atomistic Language Models Understand and Generate Materials
Jun 19, 2026Sathya Edamadaka, Krithik Ramesh, Ju Li +1Crystal Structure Graph Language Models
Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting
Jun 19, 2026Muhammad Bilal Shahid, Zhanhong Jiang, Soumik Sarkar +1Predictive Uncertainty Trajectory Prediction
BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling
Jun 19, 2026Sreevardhan Sirigiri, Weiming Zhi, Fabio RamosFlow-Based Policy Diffusion Policies
Learning the generating functional for variance reduction in lattice QCD
Jun 14, 2026Ryan Abbott, Yang Fu, Daniel C. Hackett +3Effective Field Theory Variance Reduction
From Parameters to Feature Space: Task Arithmetic for Backdoor Mitigation in Model Merging
Jun 10, 2026Zhenqian Zhu, Yamin Hu, Yiya Diao +3Continual Model Merging Backdoor Attacks
Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
Jun 9, 2026Justin Berman, Francois Charton, Andres Luna +2Integral Feynman-Kac
Guided Discovery of New Behaviors using Diffusion Policies
Jun 7, 2026Dian Yu, Sebastian Sanokowski, Majid KhadivDiffusion Policies Robot Policies
Synthics: Synthetic Physics-like Datasets for Machine Learning
Jun 4, 2026Jari VepsäläinenSynthetic Data Symbolic Regression
Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds
May 30, 2026Nathanael Tepakbong, Hanyu Hu, Chengyu Liu +1Conventional Physics-Informed Neural Networks Partial Differential Equations
GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis
May 29, 2026Jasper Post, Johannes Koch, Anton Bouter +2Symbolic Regression Multi-Gpu Systems
BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
May 7, 2026Richard Hildebrandt, Evangelos Kourlitis, Baran Hashemi +7Nuclear Data Feynman-Kac
Criticality and Saturation in Orthogonal Neural Networks
May 7, 2026Max Guillen, Jan E. GerkenTwo-Layer Neural Networks Orthogonality
Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
May 7, 2026Matias G. Delgadino, Sebastien Motsch, Advait Parulekar +2Posterior Sampling Diffusion Sampling
The Feedback Hamiltonian is the Score Function: A Diffusion-Model Framework for Quantum Trajectory Reversal
Apr 23, 2026Sagar Dubey, Alan JohnHamiltonian Feynman-Kac
ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics
Apr 1, 2026Zeyu Xia, Tyler Kim, Trevor Reed +3High Energy Physics Physics-Informed Machine Learning
Model Predictive Path Integral PID Control for Learning-Based Path Following
Mar 31, 2026Teruki Kato, Koshi Oishi, Seigo ItoModel Predictive Path Integral Model Predictive Control

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Unifying Generative Models with Path Integrals

Ramon Winterhalder

TIFLab, Dipartimento di Fisica, Università degli Studi di Milano · and INFN, Sezione di Milano, Via Celoria 16, I-20133 Milano, Italy

We formulate generative modeling as a path integral in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluation principles for a single master action. Its Martin-Siggia-Rose-Janssen-de~Dominicis (MSRJD) form separates free from interacting probability flows and opens them to diagrammatic perturbation theory. The expansion yields a one-loop correction to deterministic samplers at no stochastic-sampling cost, which we validate on solvable and nonlinear drifts, where it reduces a 53 % tree-level error to 1.6 %. Imperfect learned scores enter as insertions and yield a response-weighted score-matching objective, and symmetry-equivariant drift design becomes an operator expansion with EFT power counting.

Feynman-Kac Generative Models Perturbation Propagation

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