Paper Citation Record · LEDGER

Scaling Vision Transformers to 22 Billion Parameters

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2302.05442.

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

pith.paper-citation-record.v1
2302.05442 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:11:41.496782Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

118
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 23729b9b-4725-47c5-9564-2da4060c3202 · inbound

PaLM-E: An Embodied Multimodal Language Model cites this paper.

PaLM-E: An Embodied Multimodal Language Model Scaling Vision Transformers to 22 Billion Parameters

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:29:29.833939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e95e3818-21d5-4680-8c7c-363a6e442c85 · inbound

MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action cites this paper.

MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action Scaling Vision Transformers to 22 Billion Parameters

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T01:17:58.737145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2688c09a-5d90-4442-b90c-d2cfa5d9aac2 · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models Scaling Vision Transformers to 22 Billion Parameters

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:35:21.596114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 29ea4e0e-312f-47b3-9f67-73fb0221328b · inbound

FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer cites this paper.

FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer Scaling Vision Transformers to 22 Billion Parameters

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T10:11:41.496782Z

Source-reported events for the cited work

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Observation 9573a299-a857-4fa5-b700-90716e570d7b · inbound

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts cites this paper.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Scaling Vision Transformers to 22 Billion Parameters

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.174869Z

Source-reported events for the cited work

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Observation 2133d3de-4b97-4476-9336-66851138504e · inbound

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models cites this paper.

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models Scaling Vision Transformers to 22 Billion Parameters

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T11:44:44.160662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d02eae8-43dc-4400-8256-542870d7463b · inbound

Adversarial Attacks on Robotic Vision Language Action Models cites this paper.

Adversarial Attacks on Robotic Vision Language Action Models Scaling Vision Transformers to 22 Billion Parameters

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T11:11:40.412772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a4ac500-b51a-4f0b-8103-a72bd24ed971 · inbound

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models cites this paper.

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models Scaling Vision Transformers to 22 Billion Parameters

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:29:59.436591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d38b0cd0-40aa-4a9d-a794-0a3d11f0e51d · inbound

Scalable Object Detection in the Car Interior With Vision Foundation Models cites this paper.

Scalable Object Detection in the Car Interior With Vision Foundation Models Scaling Vision Transformers to 22 Billion Parameters

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T21:11:50.831055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation adddb5ec-da02-467e-b030-836e6a73746c · inbound

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems cites this paper.

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems Scaling Vision Transformers to 22 Billion Parameters

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T12:47:53.644339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91153d93-4a5c-4ad7-9ca3-b01c9c58f090 · inbound

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models cites this paper.

CG-MLLM: Captioning and Generating 3D content via Multi-modal Large Language Models Scaling Vision Transformers to 22 Billion Parameters

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:50:14.890183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5aa45d76-8595-4ff7-b135-eef1d3dfef7b · inbound

VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use cites this paper.

VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use Scaling Vision Transformers to 22 Billion Parameters

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:45:05.576201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4da0c83c-c463-475b-8c3a-4ccd33e04f18 · inbound

VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use cites this paper.

VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use Scaling Vision Transformers to 22 Billion Parameters

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-20T21:03:46.390770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 331735e9-43e3-47b6-941d-60a359b6b60a · inbound

VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use cites this paper.

VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use Scaling Vision Transformers to 22 Billion Parameters

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:31:22.762383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 638b18c9-252e-4496-99a5-a2168512e572 · inbound

A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability cites this paper.

A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability Scaling Vision Transformers to 22 Billion Parameters

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:38:09.371957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 927da818-1663-4882-bffc-704ad517ecdf · inbound

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor cites this paper.

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor Scaling Vision Transformers to 22 Billion Parameters

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:19:42.020874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e286a78-b870-4567-b118-a7e7fdb8cb3f · inbound

Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation cites this paper.

Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation Scaling Vision Transformers to 22 Billion Parameters

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:46.871113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f9f321a-8d8f-4e16-8130-897384d7fcbb · inbound

Unsupervised Semantic Segmentation Facilitates Model Understanding cites this paper.

Unsupervised Semantic Segmentation Facilitates Model Understanding Scaling Vision Transformers to 22 Billion Parameters

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:43:15.473340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d3d629c8-08dc-4f13-bf64-2fd1098ce959 · inbound

Unsupervised Semantic Segmentation Facilitates Model Understanding cites this paper.

Unsupervised Semantic Segmentation Facilitates Model Understanding Scaling Vision Transformers to 22 Billion Parameters

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:39:16.375380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81530ff2-576f-4bfc-a440-8cc5e3d12828 · inbound

Scaling Laws for Neural-Network Quantum States cites this paper.

Scaling Laws for Neural-Network Quantum States Scaling Vision Transformers to 22 Billion Parameters

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:27.071592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3a7d2d8c-ea92-4925-b702-774037e804a7 · inbound

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders cites this paper.

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders Scaling Vision Transformers to 22 Billion Parameters

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T07:15:29.697712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 694d21cb-4f0c-46e9-aa13-177c648bdd48 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Scaling Vision Transformers to 22 Billion Parameters

Reference 160

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:30:07.807239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3244b7a8-17a5-4b57-bf6b-2bc2a95d06ae · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Scaling Vision Transformers to 22 Billion Parameters

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T10:14:05.666991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc1682b5-aa53-4aed-9792-b9fe167a72bb · inbound

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget cites this paper.

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget Scaling Vision Transformers to 22 Billion Parameters

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T06:13:51.944737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d2c0ac0-df7d-4e71-92ba-38b944d8b4a7 · inbound

Predict before you train: Scaling Laws for particle physics foundation models cites this paper.

Predict before you train: Scaling Laws for particle physics foundation models Scaling Vision Transformers to 22 Billion Parameters

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-30T23:56:38.595543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a09046c1-03fd-451d-91a8-f87589e196ff · inbound

Opt.Gear Technical Report cites this paper.

Opt.Gear Technical Report Scaling Vision Transformers to 22 Billion Parameters

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T00:40:38.653520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 698ecde2-46b1-494a-b9a0-6406481878e0 · inbound

One QK Channel, Many Sources: Guarding Low-Precision Attention Collapse cites this paper.

One QK Channel, Many Sources: Guarding Low-Precision Attention Collapse Scaling Vision Transformers to 22 Billion Parameters

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T15:20:15.447616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.