Paper Citation Record · LEDGER

Simulation-Free Differential Dynamics through Neural Conservation Laws

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.18604.

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

pith.paper-citation-record.v1
2506.18604 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:58:32.558857Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

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verified fuzzy3
unresolved14
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External citation measurements

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Outbound references

Observation e643c22d-30c3-48a2-aec4-8bdf9ec24f29 · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

Simulation-Free Differential Dynamics through Neural Conservation Laws Building Normalizing Flows with Stochastic Interpolants

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.377515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a02e704-50cc-4c02-a075-718a4eb04e67 · outbound

This paper cites Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths.

Simulation-Free Differential Dynamics through Neural Conservation Laws Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:58:32.858374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b1798d1b-7f46-43d6-ad98-b9ea523d4c5b · outbound

This paper cites doi: 10.1109/tpami.2020.

Simulation-Free Differential Dynamics through Neural Conservation Laws doi: 10.1109/tpami.2020

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.475091Z

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Observation 6010ff7a-85ef-4f12-a9f3-71a4e1940062 · outbound

This paper cites Accelerating Motion Planning via Optimal Transport.

Simulation-Free Differential Dynamics through Neural Conservation Laws Accelerating Motion Planning via Optimal Transport

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T18:58:32.714176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 483606c8-cb41-44a7-b1bf-768cf344ac8f · outbound

This paper cites Flow Matching for Generative Modeling.

Simulation-Free Differential Dynamics through Neural Conservation Laws Flow Matching for Generative Modeling

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.483415Z

Source-reported events for the cited work

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Observation 250db038-0be7-4362-abd1-d2c96851af66 · outbound

This paper cites Generalized Schr\"odinger Bridge Matching.

Simulation-Free Differential Dynamics through Neural Conservation Laws Generalized Schr\"odinger Bridge Matching

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.487725Z

Source-reported events for the cited work

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Observation 939d23c3-f6b9-46ff-b81c-5f4ea4e686de · outbound

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

Simulation-Free Differential Dynamics through Neural Conservation Laws SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.491536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 839586bc-60c7-47d2-867a-71ad341360d4 · outbound

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

Simulation-Free Differential Dynamics through Neural Conservation Laws Score-Based Generative Modeling through Stochastic Differential Equations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.499717Z

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Observation f89994a3-3c06-44bf-98a8-7e86432b71e8 · outbound

This paper cites C PROOF OF PROPOSITION 1 Proof.We check thatρ t andut satisfy eq.

Simulation-Free Differential Dynamics through Neural Conservation Laws C PROOF OF PROPOSITION 1 Proof.We check thatρ t andut satisfy eq

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:58:33.009246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef87a6c1-00fa-4bde-ac2f-9c11eb11cab2 · outbound

This paper cites Also, the MLP parameterization along with the mixture combinations in the factorzied model turned out to be expressive enough for the experiments we have explored.

Simulation-Free Differential Dynamics through Neural Conservation Laws Also, the MLP parameterization along with the mixture combinations in the factorzied model turned out to be expressive enough for the experiments we have explored

Reference 256

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:58:32.997616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 24576981-62f2-4be9-9664-506b02be25eb · outbound

This paper cites Path integrals and symmetry breaking for optimal control theory.Journal of statistical mechanics: theory and experiment, 2005(11):P11011,.

Simulation-Free Differential Dynamics through Neural Conservation Laws Path integrals and symmetry breaking for optimal control theory.Journal of statistical mechanics: theory and experiment, 2005(11):P11011,

Reference 1989

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:58:33.175111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 04c1097f-73d7-4b19-9926-f60c6674283e · outbound

This paper cites doi: https://doi.org/10.1016/S0378-4266(03)00138-9.

Simulation-Free Differential Dynamics through Neural Conservation Laws doi: https://doi.org/10.1016/S0378-4266(03)00138-9

Reference 2004

Resolution: verified exact; doi, observed 2026-08-15T18:58:32.628468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d91b816b-59e1-492a-9d9c-e25897f81300 · outbound

This paper cites FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.

Simulation-Free Differential Dynamics through Neural Conservation Laws FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.396266Z

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Observation 7e5efae9-db0d-475e-98e6-1215793dca1b · outbound

This paper cites doi: 10.3390/e19110626.

Simulation-Free Differential Dynamics through Neural Conservation Laws doi: 10.3390/e19110626

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.400843Z

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Observation 352dc1c9-237f-4277-9302-f159acea9fdf · outbound

This paper cites PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications.

Simulation-Free Differential Dynamics through Neural Conservation Laws PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.495581Z

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Observation 80e186ff-02fc-4bd8-904c-06e5687c90e9 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Simulation-Free Differential Dynamics through Neural Conservation Laws Denoising Diffusion Probabilistic Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.405131Z

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Observation 96ce00ad-9f59-4d0a-8ecc-43ef958d8ce1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Simulation-Free Differential Dynamics through Neural Conservation Laws Adam: A Method for Stochastic Optimization

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.441078Z

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Observation 0852f9b6-5cc4-4e37-a06e-4e6c457607df · outbound

This paper cites Path Integral Sampler: a stochastic control approach for sampling.

Simulation-Free Differential Dynamics through Neural Conservation Laws Path Integral Sampler: a stochastic control approach for sampling

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.503942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91c164ad-03ae-4989-8f80-9771947dfa0a · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Simulation-Free Differential Dynamics through Neural Conservation Laws Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.382641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a0e001e-1398-4661-a56f-910f27ffce71 · outbound

This paper cites Flow Matching on General Geometries.

Simulation-Free Differential Dynamics through Neural Conservation Laws Flow Matching on General Geometries

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T18:58:32.387152Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.