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Paper Citation Record · LEDGER

Efficient Continuous Video Flow Model for Video Prediction

As of 15 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2412.05633.

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

pith.paper-citation-record.v1
2412.05633 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:35:18.850892Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

21 of 21 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ea3d59fd-4ada-4cf3-9ff4-a14e7b287a16 · outbound

This paper cites Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise.

Efficient Continuous Video Flow Model for Video Prediction Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:35:18.757771Z digest=sha256:fd1c01e06eee45ae614627f472216a16fe1ed2e5f6f6c7be46dc22f1e14e072c

Observation 6caa8ea8-d725-40fc-a34b-529f49adf3ca · outbound

This paper cites Improved Conditional VRNNs for Video Prediction.

Efficient Continuous Video Flow Model for Video Prediction Improved Conditional VRNNs for Video Prediction

Reference 4

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metadata mismatch
local_arxiv, observed 2026-08-11T20:35:19.199735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T20:35:18.766594Z digest=sha256:5e9475d3d4ef57a2f9ec0a13030c777ff5349aa921b61527affeed9d9b250b66

Observation c4041392-7bd2-40c5-8b55-907d2f653ca7 · outbound

This paper cites an unresolved cited work.

Efficient Continuous Video Flow Model for Video Prediction Unresolved cited work

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:35:18.793320Z digest=sha256:e0624b3773ca1cc4229184d1e7a82264885b521f27907dbb62540a99a91c1518

Observation 52bc8d13-c6c6-434d-b26f-575dbcd85f94 · outbound

This paper cites Stochastic Adversarial Video Prediction.

Efficient Continuous Video Flow Model for Video Prediction Stochastic Adversarial Video Prediction

Reference 14

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source=pdf_text observed=2026-08-11T20:35:18.808028Z digest=sha256:73169b0817821e055a8741e88b237d804f62e8ddc3676e82088cc6912f687a9f

Observation 1f7118f3-05de-4c19-96ef-5ac4b9ddba93 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Efficient Continuous Video Flow Model for Video Prediction Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 15

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source=pdf_text observed=2026-08-11T20:35:18.811871Z digest=sha256:1fa965f5d4ba42fded1a8c6f635fe27b41f91c927f943fd4d09b6968b326ccce

Observation ea076dd7-ca4b-4dd2-9fb6-131d9ff1f80b · outbound

This paper cites Folded Recurrent Neural Networks for Future Video Prediction.

Efficient Continuous Video Flow Model for Video Prediction Folded Recurrent Neural Networks for Future Video Prediction

Reference 17

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metadata mismatch
local_arxiv, observed 2026-08-11T20:35:19.030311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T20:35:18.818884Z digest=sha256:0fa4353130a849a89d59871a3362f72273ca65318abff184adb5352030b5dbc2

Observation 1354f494-2a51-4a73-8349-e3b930726c90 · outbound

This paper cites Latent Video Transformer.

Efficient Continuous Video Flow Model for Video Prediction Latent Video Transformer

Reference 18

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:35:18.821846Z digest=sha256:496b004597b5ca09d589593688902eea8bf56a7be42d96957cbd03080ae140ad

Observation 0cf6d955-e472-42c8-9845-53dd1a3d0fab · outbound

This paper cites Diverse Video Generation using a Gaussian Process Trigger.

Efficient Continuous Video Flow Model for Video Prediction Diverse Video Generation using a Gaussian Process Trigger

Reference 20

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source=pdf_text observed=2026-08-11T20:35:18.830752Z digest=sha256:9ad7f54ade7a9e72631f0c9e71d7b8e1bb795128db4a97d73e5e4f1d153cfd33

Observation 5b4c9b1d-5da4-4417-b96e-a3ff0d1852a1 · outbound

This paper cites Decomposing Motion and Content for Natural Video Sequence Prediction.

Efficient Continuous Video Flow Model for Video Prediction Decomposing Motion and Content for Natural Video Sequence Prediction

Reference 23

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source=pdf_text observed=2026-08-11T20:35:18.843096Z digest=sha256:63212711dfc2237b799375e0f72144646312f56244bc625b06dca875e3401d48

Observation a5a55e24-5bf5-4170-84d3-a23a177d9d94 · outbound

This paper cites VideoGPT: Video Generation using VQ-VAE and Transformers.

Efficient Continuous Video Flow Model for Video Prediction VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 24

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source=pdf_text observed=2026-08-11T20:35:18.846715Z digest=sha256:fb947cee9f862edbfbe1ca43704ca78605d5878cff6c756fba2e4abbeba9b66d

Observation d1201665-cb7c-4a45-aaed-4cd4c19061b9 · outbound

This paper cites Robust Generative Adversarial Network.

Efficient Continuous Video Flow Model for Video Prediction Robust Generative Adversarial Network

Reference 2004

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source=pdf_text observed=2026-08-11T20:35:18.826226Z digest=sha256:7a2b324ee19fa1da48af848bdbc652910e0b98c0ed26a8ed6ed4bbc9ee852d06

Observation 0ad3c4ff-2ad5-4606-9174-c14f976e2e8e · outbound

This paper cites (2022) in latent space: The latent embedding dimension for KTH, BAIR and Human3.6M is kept at 64 and 128 for the UCF101 dataset.

Efficient Continuous Video Flow Model for Video Prediction (2022) in latent space: The latent embedding dimension for KTH, BAIR and Human3.6M is kept at 64 and 128 for the UCF101 dataset

Reference 2010

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verified fuzzy
raw_fallback, observed 2026-08-11T20:35:19.237188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T20:35:18.850892Z digest=sha256:29909608c1f268a4ef48b9906a2b1ce9c40c4f4226d7d112a4e77ad3d0636f3d

Observation 97ecd655-6450-4cce-b785-cf593838867d · outbound

This paper cites Adversarial Video Generation on Complex Datasets.

Efficient Continuous Video Flow Model for Video Prediction Adversarial Video Generation on Complex Datasets

Reference 2011

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source=pdf_text observed=2026-08-11T20:35:18.771041Z digest=sha256:51b3caae816c5499dfd763e47cc6c465d87d6aa75ffd0c5d9b3f1f68d4a343e6

Observation 34b852d7-4aee-4da8-896d-d054bdb53dbf · outbound

This paper cites Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly.

Efficient Continuous Video Flow Model for Video Prediction Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly

Reference 2015

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source=pdf_text observed=2026-08-11T20:35:18.835052Z digest=sha256:646b440ab51b0d0e21bf34f0001dc78283b7479c6bf8277062b168d8674a6555

Observation 972d389a-04e6-4ce7-9a8b-2d59b2178269 · outbound

This paper cites Video Ladder Networks.

Efficient Continuous Video Flow Model for Video Prediction Video Ladder Networks

Reference 2016

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metadata mismatch
local_arxiv, observed 2026-08-11T20:35:19.171745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T20:35:18.780174Z digest=sha256:53cef6de51879f059a93d7fd13a61676338ff43758e89049d070d528316c24bd

Observation 268707c7-4eb5-4928-b35a-e90df17f1ac0 · outbound

This paper cites Elsayed, A.

Efficient Continuous Video Flow Model for Video Prediction Elsayed, A

Reference 2017

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verified fuzzy
raw_fallback, observed 2026-08-11T20:35:19.271951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T20:35:18.789144Z digest=sha256:eb684841422950fd347356df5cdf48b7acea45f74858fe5c56c1b2eb4a034a89

Observation 8d057020-5ac8-44b5-80a8-99370cd2dca7 · outbound

This paper cites Accurate grid keypoint learning for efficient video prediction.

Efficient Continuous Video Flow Model for Video Prediction Accurate grid keypoint learning for efficient video prediction

Reference 2020

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verified fuzzy
raw_fallback, observed 2026-08-11T20:35:19.249749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T20:35:18.800742Z digest=sha256:b918b9a28643f00e16cecd93fd91901613e02e7c8b1b798ae38b5fcab972743b

Observation 63bb87c6-876d-4857-82a6-4f2ff2273f44 · outbound

This paper cites FitVid: Overfitting in Pixel-Level Video Prediction.

Efficient Continuous Video Flow Model for Video Prediction FitVid: Overfitting in Pixel-Level Video Prediction

Reference 2021

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source=pdf_text observed=2026-08-11T20:35:18.752749Z digest=sha256:a0cdcda6cec80916f198ab875b44d41c71b88837b30698edca7a3a4803680c8d

Observation e41d04ef-7da9-455b-8ec6-d89619195fcb · outbound

This paper cites Auto-Encoding Variational Bayes.

Efficient Continuous Video Flow Model for Video Prediction Auto-Encoding Variational Bayes

Reference 2022

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source=pdf_text observed=2026-08-11T20:35:18.804046Z digest=sha256:d0da389dae5c685b62414ac652465fd48c312995b44fc7672a14ab9dbda72e87

Observation ec011d9f-f8a8-422e-936b-11dd01f1f942 · outbound

This paper cites Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration.

Efficient Continuous Video Flow Model for Video Prediction Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

Reference 2023

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:35:18.784070Z digest=sha256:ed2e13363c9b2c44c8e587699bc1fa0579ae0c2e08bde2c3b48d05c746e81880

Observation 810ad6f1-467b-46d2-8e2d-da48a7e644b6 · outbound

This paper cites an unresolved cited work.

Efficient Continuous Video Flow Model for Video Prediction Unresolved cited work

Reference 2024

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Pith citing papers

No inbound Pith citation observations are available.