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Paper Citation Record · LEDGER

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2510.01159.

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

pith.paper-citation-record.v1
2510.01159 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:16:00.379333Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-07T09:29:39.677622Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T09:46:26.172257Z

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 79b3b482-573d-4b5e-8827-5863860c650d · outbound

This paper cites write newline.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants write newline

Reference 1

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Observation ab2f429f-c554-4268-8a33-fe1ecbcea222 · outbound

This paper cites Wasserstein generative adversarial networks.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Wasserstein generative adversarial networks

Reference 3

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Observation b3485ebb-165b-4e8b-a90f-9ee74f8cf469 · outbound

This paper cites Robust registration of calcium images by learned contrast synthesis.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Robust registration of calcium images by learned contrast synthesis

Reference 4

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Observation 57e98021-95a8-49c1-be86-5ed39834b848 · outbound

This paper cites Learning single-cell perturbation responses using neural optimal transport.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Learning single-cell perturbation responses using neural optimal transport

Reference 5

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Observation 19a9099b-847c-4183-b5d7-34570529c80c · outbound

This paper cites Multidimensional scaling.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Multidimensional scaling

Reference 6

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Observation e423cf43-5021-4fad-a5d8-1508e400581a · outbound

This paper cites Generative adversarial nets.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Generative adversarial nets

Reference 7

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Observation 8820b047-a5d5-46e3-9372-6ecd7dda146b · outbound

This paper cites Clonal evolution in cancer.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Clonal evolution in cancer

Reference 8

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Observation 2bf2f077-c705-44e8-84f6-f94a359ea0bc · outbound

This paper cites Estimating epidemiologic dynamics from cross-sectional viral load distributions.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Estimating epidemiologic dynamics from cross-sectional viral load distributions

Reference 9

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Observation b67ab991-dc16-4a9c-8d06-a6ed10cef13b · outbound

This paper cites The GAN is dead; long live the GAN ! a modern GAN baseline.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants The GAN is dead; long live the GAN ! a modern GAN baseline

Reference 10

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Observation 200dfff8-7cdd-4efa-a4f1-3fe21cd3be72 · outbound

This paper cites The relativistic discriminator: A key element missing from standard GAN.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants The relativistic discriminator: A key element missing from standard GAN

Reference 11

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Observation 76ac8a7e-d56b-40fe-998f-d5c6412dacad · outbound

This paper cites Metric flow matching for smooth interpolations on the data manifold.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Metric flow matching for smooth interpolations on the data manifold

Reference 12

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Observation fd2282a8-21ea-4edd-beb9-6d62dcec3bb6 · outbound

This paper cites Kingma and Jimmy Ba.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Kingma and Jimmy Ba

Reference 13

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Observation 84023901-210a-4c97-8932-7fdf8000470a · outbound

This paper cites Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells

Reference 14

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Observation 6dd4e6cf-0bdc-479b-b460-33c39b976a28 · outbound

This paper cites Multimodal single cell data integration challenge: results and lessons learned.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Multimodal single cell data integration challenge: results and lessons learned

Reference 15

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Observation a5aadfb7-18f3-41ca-9307-7025496eb915 · outbound

This paper cites Multi-marginal stochastic flow matching for high-dimensional snapshot data at irregular time points.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Multi-marginal stochastic flow matching for high-dimensional snapshot data at irregular time points

Reference 16

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Observation f2a654e3-d5dc-4387-914b-fc2c22b07c9c · outbound

This paper cites Flow matching for generative modeling.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Flow matching for generative modeling

Reference 17

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Observation 8381e1fa-b28d-4372-bab0-e9f72cabae55 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 18

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Observation d06ca531-42d3-433a-a3a4-aeb25f3220e0 · outbound

This paper cites Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets

Reference 19

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Observation 76312c7e-5710-46e7-8d02-8aeefbe22042 · outbound

This paper cites The cell tracking challenge: 10 years of objective benchmarking.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants The cell tracking challenge: 10 years of objective benchmarking

Reference 20

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Observation 09adc02f-bfdf-4ab3-8367-9690291698ea · outbound

This paper cites Tumour evolution and microenvironment interactions in 2D and 3D space.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Tumour evolution and microenvironment interactions in 2D and 3D space

Reference 21

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Observation 19fac0b5-9a0d-4ae4-8f96-08ff4aaa69a3 · outbound

This paper cites Visualizing structure and transitions in high-dimensional biological data.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Visualizing structure and transitions in high-dimensional biological data

Reference 22

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Observation a8f37d21-3dca-448d-91bf-dedcbfb87171 · outbound

This paper cites A computational framework for solving Wasserstein lagrangian flows.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants A computational framework for solving Wasserstein lagrangian flows

Reference 23

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Observation fa75a564-e0ad-4579-820d-aa3b2b0bd509 · outbound

This paper cites Broken limits to life expectancy, 2002.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Broken limits to life expectancy, 2002

Reference 24

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Observation 70173b09-c98a-47b5-a417-7ce21266d6ed · outbound

This paper cites Multisample flow matching: Straightening flows with minibatch couplings.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Multisample flow matching: Straightening flows with minibatch couplings

Reference 25

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Observation c231fbf0-0a5f-4881-abbe-ef55d4ec2054 · outbound

This paper cites Modeling complex system dynamics with flow matching across time and conditions.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Modeling complex system dynamics with flow matching across time and conditions

Reference 26

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Observation b3b1f978-10ef-422a-a963-27e816324da3 · outbound

This paper cites Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

Reference 27

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Observation b300f036-8f72-453b-9ae5-42ba6963aded · outbound

This paper cites Fiji: an open-source platform for biological-image analysis.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Fiji: an open-source platform for biological-image analysis

Reference 28

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Observation fe302da4-6251-40ad-84b3-99037b2d242d · outbound

This paper cites Integrative spatial and genomic analysis of tumor heterogeneity with tumoroscope.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Integrative spatial and genomic analysis of tumor heterogeneity with tumoroscope

Reference 29

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Observation 840c5068-2ae6-4052-8afd-a45d1fc16303 · outbound

This paper cites Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Reference 30

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Observation 71fa1aa5-0882-46e4-a7fb-9c9f2b90717c · outbound

This paper cites Towards a better global loss landscape of GANs.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Towards a better global loss landscape of GANs

Reference 31

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Observation 600074e2-17b8-430e-98bb-26c14dc14b65 · outbound

This paper cites TrajectoryNet : A dynamic optimal transport network for modeling cellular dynamics.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants TrajectoryNet : A dynamic optimal transport network for modeling cellular dynamics

Reference 32

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Observation 201943c1-a790-4e2f-8baa-f6d60ca0f6ab · outbound

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

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 33

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Observation ce249338-f7d7-4dfd-b044-cb35228125a6 · outbound

This paper cites The epigenotype.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants The epigenotype

Reference 34

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Observation aa85be3c-aed8-46b2-8812-27192285473b · outbound

This paper cites Distance metric learning with application to clustering with side-information.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Distance metric learning with application to clustering with side-information

Reference 35

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Observation cf9a5b2f-80a8-47b0-9099-07b92c0c8ff2 · outbound

This paper cites Alignment and integration of spatial transcriptomics data.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Alignment and integration of spatial transcriptomics data

Reference 36

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Observation 0cda57d1-b5d9-4c41-9d48-804a764176ff · outbound

This paper cites @esa (Ref.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants @esa (Ref

Reference 37

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Observation cb972262-4ea9-45db-8664-4c5c1dd7fcbb · outbound

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Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Unresolved cited work

Reference 38

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Observation e8ce4904-7a5b-4f65-8684-481ec9667ce3 · outbound

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

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 39

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source=arxiv_source observed=2026-08-04T13:16:00.379333Z digest=sha256:a014d5d11e4939cf4e24e948f7ba7f74fd8031f5eedd3e41858a41291e331b7c

Pith citing papers

Observation 18fc3a2a-0a01-4b97-9eae-3e57f89eead3 · inbound

ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space cites this paper.

ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space Multi-Marginal Flow Matching with Adversarially Learnt Interpolants

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-07-07T02:15:57.962999Z

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.

source=pdf_text observed=2026-05-07T09:29:39.677622Z digest=sha256:5271f6674d64c45c9dd30569a14c041f2bb0e667d97dfc7a499cd296477d6fe7