Paper Citation Record · LEDGER

Machine-Learned Sampling of Conditioned Path Measures

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.01904.

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

pith.paper-citation-record.v1
2506.01904 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:40:59.087677Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact2
verified fuzzy20
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 611f7955-8e41-4658-8e7b-1f108703117f · outbound

This paper cites NETS: A Non-Equilibrium Transport Sampler.

Machine-Learned Sampling of Conditioned Path Measures NETS: A Non-Equilibrium Transport Sampler

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:37.056162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aae3e8b3-0f1f-466f-b8d0-b22c3bef7e9d · outbound

This paper cites Input convex neural networks.

Machine-Learned Sampling of Conditioned Path Measures Input convex neural networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:46.571154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ddf6c49-b24c-4688-b199-58d6455d61ca · outbound

This paper cites Sampling the posterior: An approach to non-Gaussian data assimilation.Physica D: Nonlinear Phenomena, 230(1-2):50–64, 2007.

Machine-Learned Sampling of Conditioned Path Measures Sampling the posterior: An approach to non-Gaussian data assimilation.Physica D: Nonlinear Phenomena, 230(1-2):50–64, 2007

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:13.764759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 01d4b756-532c-400b-8c5a-92778718e74a · outbound

This paper cites A computational fluid mechanics solution to the Monge- Kantorovich mass transfer problem.Numerische Mathematik, 84(3):375–393, 2000.

Machine-Learned Sampling of Conditioned Path Measures A computational fluid mechanics solution to the Monge- Kantorovich mass transfer problem.Numerische Mathematik, 84(3):375–393, 2000

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:13.647806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cceecc7e-266d-4694-a644-f036754750bd · outbound

This paper cites An entropy minimization approach to second-order variational mean-field games.Mathematical Models and Methods in Applied Sciences, 29(08):1553–1583, 2019.

Machine-Learned Sampling of Conditioned Path Measures An entropy minimization approach to second-order variational mean-field games.Mathematical Models and Methods in Applied Sciences, 29(08):1553–1583, 2019

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:13.465788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c381dfff-c509-4d49-9e9b-9f302f6a72e6 · outbound

This paper cites An augmented Lagrangian approach to Wasserstein gradient flows and applications.ESAIM: Proceedings and surveys, 54:1–17, 2016.

Machine-Learned Sampling of Conditioned Path Measures An augmented Lagrangian approach to Wasserstein gradient flows and applications.ESAIM: Proceedings and surveys, 54:1–17, 2016

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:13.240017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f2491782-74a1-41fa-a948-f8f47da4acc8 · outbound

This paper cites MCMC methods for diffusion bridges.Stochastics and Dynamics, 8(03):319–350, 2008.

Machine-Learned Sampling of Conditioned Path Measures MCMC methods for diffusion bridges.Stochastics and Dynamics, 8(03):319–350, 2008

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:13.026265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed855e48-b896-4bf3-b2a4-3a09b5ddbb79 · outbound

This paper cites an unresolved cited work.

Machine-Learned Sampling of Conditioned Path Measures Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:41:12.797463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b5089de-1888-461e-a176-48fd60f0a989 · outbound

This paper cites Proximal optimal trans- port modeling of population dynamics.

Machine-Learned Sampling of Conditioned Path Measures Proximal optimal trans- port modeling of population dynamics

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:12.581929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 539f99b0-e034-4af8-adac-ab069c23699f · outbound

This paper cites Wasserstein gradient flow of the Fisher information from a non-smooth convex minimization viewpoint.Journal of Convex Analysis, 2024.

Machine-Learned Sampling of Conditioned Path Measures Wasserstein gradient flow of the Fisher information from a non-smooth convex minimization viewpoint.Journal of Convex Analysis, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:12.348711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a6b96078-56e3-4000-a978-b5c01f56a8e9 · outbound

This paper cites Numerical Study of a Particle Method for Gradient Flows.

Machine-Learned Sampling of Conditioned Path Measures Numerical Study of a Particle Method for Gradient Flows

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:40:59.415938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f638a14e-945d-4790-bc79-d790f2ee8a90 · outbound

This paper cites Stochastic control liaisons: Richard sinkhorn meets gaspard monge on a schrodinger bridge.Siam Review, 63(2):249–313, 2021.

Machine-Learned Sampling of Conditioned Path Measures Stochastic control liaisons: Richard sinkhorn meets gaspard monge on a schrodinger bridge.Siam Review, 63(2):249–313, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:57.703916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7f8f3eb-55b4-4e41-8b97-f5b127220c53 · outbound

This paper cites Trajectory inference via mean-field langevin in path space.Advances in Neural Information Processing Systems, 35:16731–16742, 2022.

Machine-Learned Sampling of Conditioned Path Measures Trajectory inference via mean-field langevin in path space.Advances in Neural Information Processing Systems, 35:16731–16742, 2022

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:11.959770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 201a9f11-336f-474d-9efc-16bc00da09c1 · outbound

This paper cites A formula for the time derivative of the entropic cost and applications.Journal of Functional Analysis, 280(11):108964, 2021.

Machine-Learned Sampling of Conditioned Path Measures A formula for the time derivative of the entropic cost and applications.Journal of Functional Analysis, 280(11):108964, 2021

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:11.509290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dde8cbde-8a83-48a5-b736-f3ae78c7c7da · outbound

This paper cites Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling.

Machine-Learned Sampling of Conditioned Path Measures Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:57.830220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b523599-1742-4de9-b69b-2a8775d586fc · outbound

This paper cites About the analogy between optimal transport and minimal entropy.Annales de la Faculté des sciences de Toulouse: Mathématiques, 26(3):569–600, 2017.

Machine-Learned Sampling of Conditioned Path Measures About the analogy between optimal transport and minimal entropy.Annales de la Faculté des sciences de Toulouse: Mathématiques, 26(3):569–600, 2017

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:11.076364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef7740cb-fced-4b70-9ea6-e23fe33201a6 · outbound

This paper cites The Wasserstein gradient flow of the Fisher information and the quantum drift-diffusion equation.Archive for rational mechanics and analysis, 194(1):133–220, 2009.

Machine-Learned Sampling of Conditioned Path Measures The Wasserstein gradient flow of the Fisher information and the quantum drift-diffusion equation.Archive for rational mechanics and analysis, 194(1):133–220, 2009

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:10.669097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3877b787-12ca-4f0b-96fd-355ca23923c4 · outbound

This paper cites Simulating Diffusion Bridges with Score Matching.

Machine-Learned Sampling of Conditioned Path Measures Simulating Diffusion Bridges with Score Matching

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:57.963304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd4f207-8051-4871-a757-b31112ce9380 · outbound

This paper cites Stochastic optimal control for collective variable free sampling of molecular transition paths.Advances in Neural Information Processing Systems, 36, 2024.

Machine-Learned Sampling of Conditioned Path Measures Stochastic optimal control for collective variable free sampling of molecular transition paths.Advances in Neural Information Processing Systems, 36, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:10.341165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 941fd218-328a-4448-bd04-1b57e5776b66 · outbound

This paper cites Scalable gradients for stochastic differential equations.

Machine-Learned Sampling of Conditioned Path Measures Scalable gradients for stochastic differential equations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:10.116143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b68c5fe5-1f94-435d-a528-721a4de23b39 · outbound

This paper cites Stein variational gradient descent as gradient flow.Advances in neural information processing systems, 30, 2017.

Machine-Learned Sampling of Conditioned Path Measures Stein variational gradient descent as gradient flow.Advances in neural information processing systems, 30, 2017

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:09.879857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58402623-8f90-43fe-9b6d-a3149b6daff4 · outbound

This paper cites Action matching: Learning stochastic dynamics from samples.

Machine-Learned Sampling of Conditioned Path Measures Action matching: Learning stochastic dynamics from samples

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.113973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 823245e7-073f-4793-846e-072164e64d1d · outbound

This paper cites A Computational Framework for Solving Wasserstein Lagrangian Flows.

Machine-Learned Sampling of Conditioned Path Measures A Computational Framework for Solving Wasserstein Lagrangian Flows

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.184550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78589a73-d602-44f3-8b48-511583fa8bcb · outbound

This paper cites Stein transport for Bayesian inference.

Machine-Learned Sampling of Conditioned Path Measures Stein transport for Bayesian inference

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.250477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c692c25a-4483-4b38-9bce-8ee94e6641f1 · outbound

This paper cites Variational inference for stochastic differential equations.Annalen der Physik, 531(3):1800233, 2019.

Machine-Learned Sampling of Conditioned Path Measures Variational inference for stochastic differential equations.Annalen der Physik, 531(3):1800233, 2019

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.322464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6685b74-8859-4380-992c-5f636791531d · outbound

This paper cites Diffusion bridge mixture transports, Schrödinger bridge problems and generative modeling.Journal of Machine Learning Research, 24(374):1–51, 2023.

Machine-Learned Sampling of Conditioned Path Measures Diffusion bridge mixture transports, Schrödinger bridge problems and generative modeling.Journal of Machine Learning Research, 24(374):1–51, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:09.677564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3e068074-bde5-45e2-a63a-f01e488b5f3b · outbound

This paper cites Entropic approximation of Wasserstein gradient flows.SIAM Journal on Imaging Sci- ences, 8(4):2323–2351, 2015.

Machine-Learned Sampling of Conditioned Path Measures Entropic approximation of Wasserstein gradient flows.SIAM Journal on Imaging Sci- ences, 8(4):2323–2351, 2015

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:09.402830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04fd26b4-b354-413d-b261-c2f35d9f7c91 · outbound

This paper cites Conditioning Diffusions Using Malliavin Calculus.

Machine-Learned Sampling of Conditioned Path Measures Conditioning Diffusions Using Malliavin Calculus

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:40:59.247747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ed07aac-cf9b-4dda-a89d-534f06157e4d · outbound

This paper cites A variational approach to sampling in diffusion processes.

Machine-Learned Sampling of Conditioned Path Measures A variational approach to sampling in diffusion processes

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.645298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a54e69f7-e77f-41fc-bbec-e8714c8c8c6e · outbound

This paper cites Path sampling with stochastic dynamics: Some new algorithms.Journal of Computa- tional Physics, 225(1):491–508, 2007.

Machine-Learned Sampling of Conditioned Path Measures Path sampling with stochastic dynamics: Some new algorithms.Journal of Computa- tional Physics, 225(1):491–508, 2007

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:09.160106Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 467c30a0-089e-4ca8-8edb-4a4cdbfc1f34 · outbound

This paper cites World Scientific, 2010.

Machine-Learned Sampling of Conditioned Path Measures World Scientific, 2010

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.659791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e526888-aaf5-493c-b366-75719f21d545 · outbound

This paper cites Conditional path sampling of SDEs and the Langevin MCMC method.Communications in Mathematical Sciences, 2004.

Machine-Learned Sampling of Conditioned Path Measures Conditional path sampling of SDEs and the Langevin MCMC method.Communications in Mathematical Sciences, 2004

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:40:59.980944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0989879c-0276-4648-9a51-876fb74eeb28 · outbound

This paper cites A variational approach to path estimation and parameter inference of hidden diffusion processes.Journal of Machine Learning Research, 17(190):1–37, 2016.

Machine-Learned Sampling of Conditioned Path Measures A variational approach to path estimation and parameter inference of hidden diffusion processes.Journal of Machine Learning Research, 17(190):1–37, 2016

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:40:59.865027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a77ec9f0-bd1b-4219-b20d-fa85dd948718 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Machine-Learned Sampling of Conditioned Path Measures Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:58.901252Z

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Unavailable: canonical work link unavailable.

Observation 31176f3b-80be-46ed-8f55-57d05e74cc45 · outbound

This paper cites ∇ ·(πs(x)vs(x)) ! =π s(x) J(x)−E πs(x)[J(x)] =∂ sπs.

Machine-Learned Sampling of Conditioned Path Measures ∇ ·(πs(x)vs(x)) ! =π s(x) J(x)−E πs(x)[J(x)] =∂ sπs

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:40:59.701150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00bb1379-df50-4c50-8de6-aa5298a13941 · outbound

This paper cites an unresolved cited work.

Machine-Learned Sampling of Conditioned Path Measures Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:40:59.563167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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