Paper Citation Record · LEDGER

Unifying Generative Models with Path Integrals

As of 22 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2608.12438.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.12438 v1

Coverage vector

measured 32 of 32 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-16T00:24:58.264367Z

measured 32 of 32 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation f9583d2f-5c75-4937-92fd-6d879ad13dee · outbound

This paper cites Machine Learning and LHC Event Generation.

Unifying Generative Models with Path Integrals Machine Learning and LHC Event Generation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.123917Z

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Observation 65aefa24-1551-4e55-81a4-f27d5c3d3781 · outbound

This paper cites Symmetries of generating functionals of Langevin processes with colored multiplicative noise.

Unifying Generative Models with Path Integrals Symmetries of generating functionals of Langevin processes with colored multiplicative noise

Reference 14

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Observation 330dc932-8f8d-4058-bf59-c1a871c2222a · outbound

This paper cites Generative Adversarial Networks.

Unifying Generative Models with Path Integrals Generative Adversarial Networks

Reference 20

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Observation 07adfff5-2432-493b-a9d5-eeee8c808f43 · outbound

This paper cites Stabilizing Training of Generative Adversarial Networks through Regularization.

Unifying Generative Models with Path Integrals Stabilizing Training of Generative Adversarial Networks through Regularization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.220030Z

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Observation 79c5dd2f-a1e1-42ea-ac8c-7311ad32c734 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Unifying Generative Models with Path Integrals Spectral Normalization for Generative Adversarial Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.230042Z

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Observation 74646452-1bc9-4e33-a36f-a9b2431b9466 · outbound

This paper cites Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models.

Unifying Generative Models with Path Integrals Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.239796Z

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Observation a17ae5ed-d21a-4201-a086-1ffc52b53037 · outbound

This paper cites Weinberg,Phenomenological Lagrangians, Physica A96(1979) 1-2,.

Unifying Generative Models with Path Integrals Weinberg,Phenomenological Lagrangians, Physica A96(1979) 1-2,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:24:58.594922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d62a4b80-1095-4323-ab14-c0323813228c · outbound

This paper cites An Effective Field Theory of Gravity for Extended Objects.

Unifying Generative Models with Path Integrals An Effective Field Theory of Gravity for Extended Objects

Reference 288

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Observation 1fe0a88d-560d-4c59-a013-7b7d93bc1ba4 · outbound

This paper cites an unresolved cited work.

Unifying Generative Models with Path Integrals Unresolved cited work

Reference 313

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:24:58.678197Z

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Observation 28da0806-5b2e-4d5d-9e70-c4f50ff8466f · outbound

This paper cites Weinberg,Nuclear forces from chiral Lagrangians, Phys.

Unifying Generative Models with Path Integrals Weinberg,Nuclear forces from chiral Lagrangians, Phys

Reference 327

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:24:58.580498Z

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Observation dfd00d31-fa11-4e52-a2dc-5036625c99c8 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Unifying Generative Models with Path Integrals Score-Based Generative Modeling through Stochastic Differential Equations

Reference 377

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Observation 83eb7d0a-3f65-42cf-ad73-d354f79de145 · outbound

This paper cites Hubbard,Calculation of partition functions, Phys.

Unifying Generative Models with Path Integrals Hubbard,Calculation of partition functions, Phys

Reference 416

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:24:58.608720Z

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Observation 7bc37107-12ff-4cd1-bdc0-0418de3413cf · outbound

This paper cites [11]H.-K.

Unifying Generative Models with Path Integrals [11]H.-K

Reference 423

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.140829Z

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Observation f42b6ef9-bf25-406d-8c89-6ba0dc8cbbef · outbound

This paper cites Maruyama,Continuous markov processes and stochastic equations, Rendiconti del Circolo Matematico di Palermo4(1955).

Unifying Generative Models with Path Integrals Maruyama,Continuous markov processes and stochastic equations, Rendiconti del Circolo Matematico di Palermo4(1955)

Reference 1188

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:24:58.664561Z

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Observation a9af921a-26f0-4ead-9b42-ef4a80df90d5 · outbound

This paper cites Machlup and L.

Unifying Generative Models with Path Integrals Machlup and L

Reference 1505

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:24:58.718868Z

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Observation 9a3359b4-3e49-41cf-890a-64c9a4d8c4e0 · outbound

This paper cites A Guide to Constraining Effective Field Theories with Machine Learning.

Unifying Generative Models with Path Integrals A Guide to Constraining Effective Field Theories with Machine Learning

Reference 1661

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Observation 78581eaa-2d17-4211-9f87-03512077556f · outbound

This paper cites Zinn-Justin,Quantum field theory and critical phenomena, Int.

Unifying Generative Models with Path Integrals Zinn-Justin,Quantum field theory and critical phenomena, Int

Reference 1965

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:24:58.636698Z

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Observation 318bdc09-75fd-4e61-b77e-574a4e059030 · outbound

This paper cites A survey of the Schr\"odinger problem and some of its connections with optimal transport.

Unifying Generative Models with Path Integrals A survey of the Schr\"odinger problem and some of its connections with optimal transport

Reference 1984

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.205393Z

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Observation a6dc7acb-0348-4223-bd5e-25d8551b0eae · outbound

This paper cites an unresolved cited work.

Unifying Generative Models with Path Integrals Unresolved cited work

Reference 1989

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:24:58.691592Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aaa3784d-642c-464c-96d0-6dfea1db0a96 · outbound

This paper cites an unresolved cited work.

Unifying Generative Models with Path Integrals Unresolved cited work

Reference 1992

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:24:58.650916Z

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Observation 80056a02-0c4a-418b-ba88-53f8cb361853 · outbound

This paper cites an unresolved cited work.

Unifying Generative Models with Path Integrals Unresolved cited work

Reference 2006

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:24:58.705323Z

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Observation ee0ef04e-0183-4b6d-80dd-e30ba0b8c0ca · outbound

This paper cites Vincent,A connection between score matching and denoising autoencoders, Neural Computation23(2011) 7,.

Unifying Generative Models with Path Integrals Vincent,A connection between score matching and denoising autoencoders, Neural Computation23(2011) 7,

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:24:58.622467Z

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Observation 84e931a6-4326-4fd3-bd4d-f1dedcb553fb · outbound

This paper cites Auto-Encoding Variational Bayes.

Unifying Generative Models with Path Integrals Auto-Encoding Variational Bayes

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.161934Z

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Observation ebf1ca19-5bd8-42c8-8732-5f462c0c96e9 · outbound

This paper cites Towards Principled Methods for Training Generative Adversarial Networks.

Unifying Generative Models with Path Integrals Towards Principled Methods for Training Generative Adversarial Networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.215180Z

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Observation d7bf1665-78bd-4e92-9b61-0961e24976fd · outbound

This paper cites Which Training Methods for GANs do actually Converge?.

Unifying Generative Models with Path Integrals Which Training Methods for GANs do actually Converge?

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.225123Z

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Observation b9538ec6-78b9-4fda-935a-0dc2a689957c · outbound

This paper cites SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows.

Unifying Generative Models with Path Integrals SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.151608Z

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Observation 0522edc0-25af-4ffd-b334-9fdbc447d982 · outbound

This paper cites Maximum Likelihood Training of Score-Based Diffusion Models.

Unifying Generative Models with Path Integrals Maximum Likelihood Training of Score-Based Diffusion Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.195822Z

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Observation e4892bb5-6b54-4613-9385-675efd7b5077 · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

Unifying Generative Models with Path Integrals Elucidating the Design Space of Diffusion-Based Generative Models

Reference 2022

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Observation 80fe97e9-77a4-492a-bf16-8ee90abea708 · outbound

This paper cites Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation.

Unifying Generative Models with Path Integrals Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.200497Z

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Observation d8b6a54e-9001-4822-8fd9-141eedbd7721 · outbound

This paper cites AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models.

Unifying Generative Models with Path Integrals AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models

Reference 2024

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Observation 6dadd1db-97f3-48a4-b27e-3b817830f7a7 · outbound

This paper cites Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.

Unifying Generative Models with Path Integrals Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-16T00:24:58.253593Z

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Observation 641fbd72-42b2-4488-b7ce-20f4a134b8a9 · outbound

This paper cites The Living Guide of Machine Learning for Particle Physics.

Unifying Generative Models with Path Integrals The Living Guide of Machine Learning for Particle Physics

Reference 2026

Resolution: verified exact; local_arxiv, observed 2026-08-16T00:24:58.551390Z

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Pith citing papers

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