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Paper Citation Record · LEDGER

Native Extrapolation Awareness in Flow-Based Conditional Generation

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2602.13061.

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

pith.paper-citation-record.v1
2602.13061 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:42:37.159426Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

27 of 27 outbound references displayed

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Outbound references

Observation 2ccbe51f-9600-47cf-9b25-f62896da5b38 · outbound

This paper cites WAIC, but Why? Generative Ensembles for Robust Anomaly Detection.

Native Extrapolation Awareness in Flow-Based Conditional Generation WAIC, but Why? Generative Ensembles for Robust Anomaly Detection

Reference 5

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source=pdf_text observed=2026-08-02T23:42:34.465650Z digest=sha256:091c303db043fc09a8e921a5cc7d4629dd4bf2d058378c8ac9d2c21361c6828d

Observation ba44b2aa-19ca-4969-a7be-ab6db9ac0214 · outbound

This paper cites 2023.3282993.

Native Extrapolation Awareness in Flow-Based Conditional Generation 2023.3282993

Reference 7

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source=pdf_text observed=2026-08-02T23:42:34.743034Z digest=sha256:9fb0f3e5a7e8a45164555d9a86978c7f8d5687767ac9e94b37bed3696ea2a264

Observation c0c60c8c-27bd-41ab-903f-341040e04c98 · outbound

This paper cites L., Wang, F.-Y ., Herrera-Viedma, E., and Herrera, F.

Native Extrapolation Awareness in Flow-Based Conditional Generation L., Wang, F.-Y ., Herrera-Viedma, E., and Herrera, F

Reference 9

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Observation ddacfedd-0c78-4647-bf80-4647533343db · outbound

This paper cites J., H´olm, E., Janiskov´a, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Th ´epaut, J.-N.

Native Extrapolation Awareness in Flow-Based Conditional Generation J., H´olm, E., Janiskov´a, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Th ´epaut, J.-N

Reference 10

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source=pdf_text observed=2026-08-02T23:42:35.095197Z digest=sha256:4d620e8b478e31b7239da317dcf4208afa1b4d1d456bd5162c37438a343db0c5

Observation 19180c03-7471-4602-beab-8cf5309896b2 · outbound

This paper cites Generalized Consistency Trajectory Models for Image Manipulation.

Native Extrapolation Awareness in Flow-Based Conditional Generation Generalized Consistency Trajectory Models for Image Manipulation

Reference 13

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source=pdf_text observed=2026-08-02T23:42:35.395076Z digest=sha256:a2bb018c36bfdcb8d5514a0235edba7a95ef2d60541343432ee3628a95852a18

Observation e396c8f7-ce7e-4816-a017-482bf01f9ca8 · outbound

This paper cites Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting.

Native Extrapolation Awareness in Flow-Based Conditional Generation Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting

Reference 14

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source=pdf_text observed=2026-08-02T23:42:35.498568Z digest=sha256:2adca7ed8b6204425491d8eaec1bee04dda7ff5795c00cbf02f160e0e0ed1e6d

Observation 0dd62c57-71dc-4cb5-a269-8d192fa01a8a · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

Native Extrapolation Awareness in Flow-Based Conditional Generation Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 15

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source=pdf_text observed=2026-08-02T23:42:35.597153Z digest=sha256:6c12281842d7431ffb916e7abd2d4f451ed78ad7cef37c89cdb648b33b78495e

Observation 1b657c93-f229-4ccb-ba99-ddfdf4b1b3db · outbound

This paper cites Do Deep Generative Models Know What They Don't Know?.

Native Extrapolation Awareness in Flow-Based Conditional Generation Do Deep Generative Models Know What They Don't Know?

Reference 19

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Observation ffe9c9ca-0176-4d1a-b0af-545eb49331c1 · outbound

This paper cites Y ., et al.

Native Extrapolation Awareness in Flow-Based Conditional Generation Y ., et al

Reference 20

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source=pdf_text observed=2026-08-02T23:42:36.183846Z digest=sha256:7c00e5180e51e98136d6e113792879936d6bf64e254fa10d20b7baba0411d7c2

Observation b662e099-3a64-48af-be54-918eed490f37 · outbound

This paper cites Extremum Flow Matching for Offline Goal Conditioned Reinforcement Learning.

Native Extrapolation Awareness in Flow-Based Conditional Generation Extremum Flow Matching for Offline Goal Conditioned Reinforcement Learning

Reference 21

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source=pdf_text observed=2026-08-02T23:42:36.301802Z digest=sha256:5d1b56361ccf3e10bef0e84fb935818ace5112c9aead535c69a0a8f6b21c34a9

Observation 961ee42b-2fc7-4372-b020-1ef13f6036a1 · outbound

This paper cites Input complexity and out-of-distribution detection with likelihood-based generative models.

Native Extrapolation Awareness in Flow-Based Conditional Generation Input complexity and out-of-distribution detection with likelihood-based generative models

Reference 22

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source=pdf_text observed=2026-08-02T23:42:36.462013Z digest=sha256:10fd1475c16134bcbe5d52d31daec819e50d7835de4fcbaf551b91a1c9212ce5

Observation 35d755a9-7dd6-4da3-b5c0-970fb207b205 · outbound

This paper cites Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S.

Native Extrapolation Awareness in Flow-Based Conditional Generation Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S

Reference 23

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source=pdf_text observed=2026-08-02T23:42:36.550943Z digest=sha256:c45c87b117300ac34e216566d3410f44efd5f506caeef26b63246758eb109ee2

Observation 89144b33-3faf-4ed8-bc4e-65a35ae3fb91 · outbound

This paper cites P., and Bovik, A.

Native Extrapolation Awareness in Flow-Based Conditional Generation P., and Bovik, A

Reference 24

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source=pdf_text observed=2026-08-02T23:42:36.687919Z digest=sha256:1c87185cbb2227ff9a06e9b1d3c5c05dbaf62d46b68e81b8d26edce34aed5d4a

Observation 6dd46c47-a2b6-4ace-84c8-1d1c6633d238 · outbound

This paper cites Unlike simple shapes, a spiral represents a stiff geometric structure where the optimal transport path (a straight line) often intersects regions that are off-manifold.

Native Extrapolation Awareness in Flow-Based Conditional Generation Unlike simple shapes, a spiral represents a stiff geometric structure where the optimal transport path (a straight line) often intersects regions that are off-manifold

Reference 26

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source=pdf_text observed=2026-08-02T23:42:36.991663Z digest=sha256:ceb16b1ff21c3fc3f33f5683e70c470e37216c97ca5b1fd8069a3207502002dc

Observation 550c3029-36a0-4c7f-b0e4-c2bbd23025cf · outbound

This paper cites an unresolved cited work.

Native Extrapolation Awareness in Flow-Based Conditional Generation Unresolved cited work

Reference 64

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source=pdf_text observed=2026-08-02T23:42:37.159426Z digest=sha256:cdf5c328b1ed2045552c9b2b83a2d885941b301b5e4d27bcdd5ecd184ec971df

Observation 727b5131-f3d5-4ae0-b644-8a75f3c911ac · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Native Extrapolation Awareness in Flow-Based Conditional Generation $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 2000

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source=pdf_text observed=2026-08-02T23:42:34.070870Z digest=sha256:08047585fa251093d6edc0c281db261822ea67eff9b9ec78e4f8a80ca98a7d08

Observation d73ba675-52af-4df0-9ba0-3095e20103c1 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Native Extrapolation Awareness in Flow-Based Conditional Generation Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2004

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source=pdf_text observed=2026-08-02T23:42:36.829210Z digest=sha256:363f37791dc23b003c2f13f4b7c214be87df83b94226fed20e8fbaf850737613

Observation ad412f8e-35a7-4997-8559-dd5152457c4c · outbound

This paper cites Deep Anomaly Detection with Outlier Exposure.

Native Extrapolation Awareness in Flow-Based Conditional Generation Deep Anomaly Detection with Outlier Exposure

Reference 2006

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source=pdf_text observed=2026-08-02T23:42:34.848229Z digest=sha256:2b7a8fed8b8737f2c759619ea978b6554706b4bd3dd7d4473dd5b0aead3c34f8

Observation 8141c68d-dd68-46dc-bc6d-6fe01fd64ab6 · outbound

This paper cites Alphafold meets flow matching for generating protein ensembles.

Native Extrapolation Awareness in Flow-Based Conditional Generation Alphafold meets flow matching for generating protein ensembles

Reference 2010

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source=pdf_text observed=2026-08-02T23:42:35.290977Z digest=sha256:3cacb8cb9d37e5c5d9f0471c66f043dd2af6b150c33f8d8cf5ce9c546bab56d0

Observation 63d715ac-76ce-4e32-bb9b-93e2d01707bd · outbound

This paper cites Flow Matching for Generative Modeling.

Native Extrapolation Awareness in Flow-Based Conditional Generation Flow Matching for Generative Modeling

Reference 2017

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source=pdf_text observed=2026-08-02T23:42:35.692310Z digest=sha256:01093f5b03d8dbc73d852a02673838140c5091775c72c2ae908ea01d0aae87fb

Observation 3607f233-c0b7-4508-b87a-b20bd74d1b1f · outbound

This paper cites Deep Learning for Classical Japanese Literature.

Native Extrapolation Awareness in Flow-Based Conditional Generation Deep Learning for Classical Japanese Literature

Reference 2018

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source=pdf_text observed=2026-08-02T23:42:34.584691Z digest=sha256:84f84cb5429668a680dd60df12fae895064b94a1ad096b4f29518223032ad896

Observation 5196717a-5766-4c8b-9efa-c06e72e4e775 · outbound

This paper cites Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S.

Native Extrapolation Awareness in Flow-Based Conditional Generation Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S

Reference 2020

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source=pdf_text observed=2026-08-02T23:42:35.212432Z digest=sha256:e24461ae953bfad0ab8692e0ea1f469e51e5fd25b42b1c3de8d7e0b2a5298c8f

Observation d8392651-4b6f-44d8-9e84-93380322fb20 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Native Extrapolation Awareness in Flow-Based Conditional Generation Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 2021

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source=pdf_text observed=2026-08-02T23:42:35.944152Z digest=sha256:ea12e79f47da88d42a45385c7cad961d0b86e4eda4f430079cf039e45caf2d54

Observation 60c9391b-6d5e-4b5f-bd4d-dddac5b939aa · outbound

This paper cites Flow Matching Guide and Code.

Native Extrapolation Awareness in Flow-Based Conditional Generation Flow Matching Guide and Code

Reference 2022

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source=pdf_text observed=2026-08-02T23:42:35.816808Z digest=sha256:f1a57812b96184c26a38b7b45790619ef83aafb9a4afd39afb2f89c31aa06112

Observation c0cb08ac-332b-40ab-b29f-788ecd8b865d · outbound

This paper cites Deep Learning for Anomaly Detection: A Survey.

Native Extrapolation Awareness in Flow-Based Conditional Generation Deep Learning for Anomaly Detection: A Survey

Reference 2023

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source=pdf_text observed=2026-08-02T23:42:34.354292Z digest=sha256:ef8786f52d35731647bb0f34bea1511488bddf1d7400cbacef0fa2d0d57d4e1d

Observation aaafb032-d56c-454e-a3a6-b78559602781 · outbound

This paper cites SE(3)-Stochastic Flow Matching for Protein Backbone Generation.

Native Extrapolation Awareness in Flow-Based Conditional Generation SE(3)-Stochastic Flow Matching for Protein Backbone Generation

Reference 2024

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source=pdf_text observed=2026-08-02T23:42:34.216040Z digest=sha256:3edd9ea357cd96c3970e4620c3c0b0701c1f284480728e17743afcd4c1cc80af

Observation a75db71d-cd8e-4bc1-b818-d76ea7fb8862 · outbound

This paper cites Theoretical Foundations of Conformal Prediction.

Native Extrapolation Awareness in Flow-Based Conditional Generation Theoretical Foundations of Conformal Prediction

Reference 2025

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source=pdf_text observed=2026-08-02T23:42:33.957982Z digest=sha256:fdee30b1c9ef4eeeed5fa655ec626732cc14b5812a46ad8752f70ec7916acb5e

Pith citing papers

No inbound Pith citation observations are available.