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Paper Citation Record · LEDGER

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2301.00808.

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

pith.paper-citation-record.v1
2301.00808 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:32:18.818455Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

65
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 378c1f21-a5ac-4a24-a2e1-d857524c7019 · inbound

MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling cites this paper.

MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 31

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no resolver link, observed 2026-08-09T19:32:18.818455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6e349ed2-2c6d-4de5-9aca-a44cb211432d · inbound

RaPA: Enhancing Transferable Targeted Attacks via Random Parameter Pruning cites this paper.

RaPA: Enhancing Transferable Targeted Attacks via Random Parameter Pruning ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 53

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verified exact
arxiv_id, observed 2026-05-22T18:41:56.447002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 9c340e9d-7bb0-4343-a145-7ed149a86112 · inbound

Mahalanobis++: Improving OOD Detection via Feature Normalization cites this paper.

Mahalanobis++: Improving OOD Detection via Feature Normalization ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 50

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no resolver link, observed 2026-08-07T14:40:24.811478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2b3e19f3-5e97-427e-ade1-76425bf5ffbd · inbound

SIM-Net: A Multimodal Fusion Network Using Inferred 3D Object Shape Point Clouds from RGB Images for 2D Classification cites this paper.

SIM-Net: A Multimodal Fusion Network Using Inferred 3D Object Shape Point Clouds from RGB Images for 2D Classification ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 52

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no resolver link, observed 2026-08-06T23:21:46.234887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:21:46.234887Z digest=sha256:b3efac9786b65bedc72508c10e8790ccf5dfcc538d761ae829dc6bb88c7dcb6c

Observation b46d0db2-d86b-4a13-aca4-ba627ae1c8c1 · inbound

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing cites this paper.

AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 29

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no resolver link, observed 2026-08-06T22:54:21.901862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:54:21.901862Z digest=sha256:ed6bbdee67d0475c7263451b8544bf3eb84a83311730171221631b606d43f631

Observation d6bbc16f-a727-4250-a1f4-4c74e3d8f231 · inbound

MVGBench: Comprehensive Benchmark for Multi-view Generation Models cites this paper.

MVGBench: Comprehensive Benchmark for Multi-view Generation Models ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 59

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no resolver link, observed 2026-08-07T04:52:09.842949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:52:09.842949Z digest=sha256:c2850c1160ad63a4634db519f40e1372acf2c454179be791b1f4580bf4d43512

Observation f0ff0cf9-3c50-4858-9037-a99cb6c6d5d9 · inbound

On the rankability of visual embeddings cites this paper.

On the rankability of visual embeddings ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 61

Resolution
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no resolver link, observed 2026-08-06T20:11:51.656002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:11:51.656002Z digest=sha256:2928f24fc833fccc01b9f764e41b9a70dda5c75749fc27cc8d49aa4ab065dc79

Observation c7567e3a-c51c-4aca-94ff-18800bf341a7 · inbound

QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models cites this paper.

QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 54

Resolution
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no resolver link, observed 2026-08-06T17:59:31.845406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5337e5b9-e382-4e1a-ab0c-9c1b96ce9150 · inbound

Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams cites this paper.

Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 17

Resolution
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no resolver link, observed 2026-08-06T17:54:52.318302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:54:52.318302Z digest=sha256:f4f3f11e0a3eb5e46025eaf9fa56bf18a826f3e40db2b826fe8c1d7b50b87b45

Observation 9ff89682-cb2b-44f4-881e-5dc0e65bc7a4 · inbound

WaveLLDM: Design and Development of a Lightweight Latent Diffusion Model for Speech Enhancement and Restoration cites this paper.

WaveLLDM: Design and Development of a Lightweight Latent Diffusion Model for Speech Enhancement and Restoration ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 56

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no resolver link, observed 2026-08-05T14:36:37.710782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:36:37.710782Z digest=sha256:2fdcae697cb27b64f17fb7e86fbb264bd59045db523abf1317e2068493c6e390

Observation 6d711f93-5892-4d1c-b25f-961151d679cc · inbound

Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training cites this paper.

Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:41:26.791263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation a5077056-906a-4a6e-9c18-94c0b940d504 · inbound

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing cites this paper.

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 49

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metadata mismatch
arxiv_id, observed 2026-05-19T16:22:39.089429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-19T16:21:02.198882Z digest=sha256:6feacff6a78ee6a39dd7e6e6f437ed1901b27a1ff039d68193329d974ed1ac53

Observation 080d9590-96bd-4fa8-a406-c0b0c1c37ce6 · inbound

Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.

Toward Calibrated, Fair, and accurate Deepfake Detection ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 84

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metadata mismatch
arxiv_id, observed 2026-06-28T07:11:45.358496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T07:05:18.026601Z digest=sha256:d35f167351419dbf1a663b0ecc2bb32c36525b4d3e7c8659a726b459b19a4149

Observation cae7ec2e-35c0-4e5c-b240-fa1d53effa90 · inbound

Fourier Features Let Agents Learn High Precision Policies with Imitation Learning cites this paper.

Fourier Features Let Agents Learn High Precision Policies with Imitation Learning ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 50

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verified exact
arxiv_id, observed 2026-07-03T08:47:50.422641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-27T10:42:50.638059Z digest=sha256:2b95b6c3f5106d5007e2cfd8bf58470ddba7f72cf8fd85146679ff9f136e14a5

Observation ac30da71-55cc-471a-8978-be414f433c7c · inbound

Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific cites this paper.

Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T19:28:52.809551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T01:45:26.525737Z digest=sha256:8b418621ce917097715a26af38da2fcf93c9788415ef07ade2e6cdaa0c0eeb5d

Observation 5288c427-5cbb-4bb3-8dbf-18cadb553a7b · inbound

Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion cites this paper.

Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 35

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metadata mismatch
arxiv_id, observed 2026-07-03T23:59:07.345949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T21:32:27.296146Z digest=sha256:510a8e69b80bcb5a6027836c24ef3ac5a66a6895006d161fbb1340599682e0ed

Observation 54e90850-509c-4a3e-9424-8a4569438ab4 · inbound

Liquid Fusion of Heterogeneous Representations Towards General Salient Object Detection cites this paper.

Liquid Fusion of Heterogeneous Representations Towards General Salient Object Detection ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 67

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metadata mismatch
arxiv_id, observed 2026-07-04T12:59:52.166691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation c27a1dcc-5dec-4ec5-b254-aba887f7f36d · inbound

Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy cites this paper.

Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 37

Resolution
unresolved
no resolver link, observed 2026-07-11T22:45:34.273188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4f596b46-9803-4a03-910d-d7e4ce14da2a · inbound

AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism cites this paper.

AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-07-10T05:56:50.442376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation cabea251-e2a0-4456-81cf-d9e469812438 · inbound

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification cites this paper.

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-02T07:41:41.358271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T07:41:41.358271Z digest=sha256:8aaaf06d4e7fd7984494c194a63f2c86f23429b703c96c3cb5c22846047aac90

Observation 4f1a7d8f-f173-4671-b80b-e21beb226ea5 · inbound

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction cites this paper.

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 32

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no resolver link, observed 2026-07-30T23:02:40.103651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T23:02:40.103651Z digest=sha256:d18d6c23f4e079a3d26da68912dba3d74105a14762b89d5081af6b8d098ab296

Observation 9d7e75ee-75c0-47c8-ba4d-d9fddb09818d · inbound

AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes cites this paper.

AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-01T08:12:45.329350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9cb226fb-7b5f-4f81-b452-eecce1b3398c · inbound

LaPrune: Controllable Differentiable Sparsity at Million Scale cites this paper.

LaPrune: Controllable Differentiable Sparsity at Million Scale ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 43

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unresolved
no resolver link, observed 2026-08-08T00:53:24.638438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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