Paper Citation Record · LEDGER

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference

As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.16260.

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

pith.paper-citation-record.v1
2507.16260 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:19:59.110224Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

46 of 46 outbound references displayed

verified exact2
verified fuzzy27
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9af22605-017b-459b-a20c-567cd3042483 · outbound

This paper cites Attention is all you need,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Attention is all you need,

Reference 1

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Observation 0cc6bdb9-1adf-4c2f-96d5-0deb8ff02b75 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

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Observation 625c183d-304e-478e-ac5a-1e66d08d1c75 · outbound

This paper cites (2022) Introducing chatgpt.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference (2022) Introducing chatgpt

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:20:00.180963Z

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Observation 3b0154b0-7f2c-4e98-9df6-d4c2dbb78b5b · outbound

This paper cites (2023) Github copilot: Your ai pair programmer.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference (2023) Github copilot: Your ai pair programmer

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:20:00.146757Z

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Observation 9d22507a-d8a7-4c12-a8a2-e241f0284866 · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Training data-efficient image transformers & distillation through attention,

Reference 5

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Observation d2f7a58c-099c-402e-a65f-582dd39f2fee · outbound

This paper cites Tinymim: An empirical study of distilling mim pre-trained models,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Tinymim: An empirical study of distilling mim pre-trained models,

Reference 6

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Observation d587a21c-d107-43c5-a37c-bd25f3be3deb · outbound

This paper cites Llm-pruner: On the structural pruning of large language models,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Llm-pruner: On the structural pruning of large language models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:58.882285Z

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Observation 78efd689-fbdd-4af7-a373-282771e77cf3 · outbound

This paper cites Width & depth pruning for vision transformers,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Width & depth pruning for vision transformers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:20:00.025120Z

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Observation fe4a3eab-9de8-4e80-ab80-8c3fba1aba3a · outbound

This paper cites Towards accurate post-training quantization for vision transformer,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Towards accurate post-training quantization for vision transformer,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.993363Z

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Observation 62356d5e-b19f-4ac8-b4a9-b25d4f2ed401 · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.972101Z

Source-reported events for the cited work

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Observation 19e15b13-674d-47e8-a099-3970fb6770cb · outbound

This paper cites Co-scale conv-attentional image transformers,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Co-scale conv-attentional image transformers,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.952342Z

Source-reported events for the cited work

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Observation 572eed05-64be-4d63-9395-b1fc0ed5187e · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 12

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Source-reported events for the cited work

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Observation 5924a9e0-d702-4f04-bc33-fc69fadcd86b · outbound

This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Tokens-to-token vit: Training vision transformers from scratch on imagenet,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.916326Z

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Observation 87a9b2e3-3c2f-46d2-94f8-a85bd043f075 · outbound

This paper cites Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:58.923885Z

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Observation e12db6a7-43fa-4d7e-ba8c-f503893a1a2b · outbound

This paper cites Dynam- icvit: Efficient vision transformers with dynamic token sparsification,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Dynam- icvit: Efficient vision transformers with dynamic token sparsification,

Reference 15

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Source-reported events for the cited work

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Observation 70cd872a-877b-43c8-8948-f5ab71a62886 · outbound

This paper cites Token merging: Your vit but faster,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Token merging: Your vit but faster,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.873298Z

Source-reported events for the cited work

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Observation 1f88df41-913b-4475-aacd-ea987b69ac93 · outbound

This paper cites Adaptive sparse vit: towards learnable adaptive token pruning by fully exploiting self-attention,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Adaptive sparse vit: towards learnable adaptive token pruning by fully exploiting self-attention,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.853885Z

Source-reported events for the cited work

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Observation abacbcaa-26e1-43f2-800d-1412e5f28a1c · outbound

This paper cites A simple romance between multi-exit vision transformer and token reduction,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference A simple romance between multi-exit vision transformer and token reduction,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.831841Z

Source-reported events for the cited work

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Observation 832cfb95-3282-47cf-842f-523cf4fa28d0 · outbound

This paper cites Synergistic patch pruning for vision transformer: Unifying intra-& inter-layer patch importance,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Synergistic patch pruning for vision transformer: Unifying intra-& inter-layer patch importance,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.809147Z

Source-reported events for the cited work

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Observation 5a02c8ef-48f5-45be-9de3-930a74b587db · outbound

This paper cites Diffrate: Differentiable compression rate for efficient vision transformers,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Diffrate: Differentiable compression rate for efficient vision transformers,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.784661Z

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Observation f2aff503-aa81-4511-8807-d79ad442c39f · outbound

This paper cites Beyond attentive tokens: Incorporating token importance and diversity for efficient vision transformers,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Beyond attentive tokens: Incorporating token importance and diversity for efficient vision transformers,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.759181Z

Source-reported events for the cited work

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Observation f77f8566-6718-4936-80e4-fea8cb6fb691 · outbound

This paper cites Joint token pruning and squeezing towards more aggressive compression of vision transformers,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Joint token pruning and squeezing towards more aggressive compression of vision transformers,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.736637Z

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Observation 2de641b9-64a4-4e3b-9b16-e1caf65f9fd3 · outbound

This paper cites All Tokens Matter: Token Labeling for Training Better Vision Transformers.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference All Tokens Matter: Token Labeling for Training Better Vision Transformers

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:19:59.317187Z

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Observation 5af4db4f-f28f-4827-bbbe-0e19e78aa40e · outbound

This paper cites [Online].

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference [Online]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.713468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fb95ba81-c319-4833-95a2-07d6ddd47cfc · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Categorical Reparameterization with Gumbel-Softmax

Reference 25

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Source-reported events for the cited work

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Observation 568dd40d-ccb7-4160-b96e-34cc1a7a87c4 · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:58.996503Z

Source-reported events for the cited work

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Observation 4df2c928-24a3-4f30-97a7-78f5eebbe469 · outbound

This paper cites All tokens matter: Token labeling for training better vi- sion transformers,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference All tokens matter: Token labeling for training better vi- sion transformers,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.685878Z

Source-reported events for the cited work

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Observation 8cc1c5bf-8968-4ad3-9eca-ff44277872a7 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Imagenet: A large-scale hierarchical image database,

Reference 28

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Source-reported events for the cited work

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Observation cb0d1b08-096d-49d0-aa98-1f546ab89995 · outbound

This paper cites Crossvit: Cross-attention multi- scale vision transformer for image classification,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Crossvit: Cross-attention multi- scale vision transformer for image classification,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.647920Z

Source-reported events for the cited work

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Observation 8987f68c-8c40-440b-bc33-52f6ac536e7a · outbound

This paper cites Conditional Positional Encodings for Vision Transformers.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Conditional Positional Encodings for Vision Transformers

Reference 30

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Observation 44b3072c-8fde-4962-9483-13ed35662064 · outbound

This paper cites Designing network design spaces,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Designing network design spaces,

Reference 31

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Observation 3d830739-c10d-4ffa-8adb-fb5655c9ef7d · outbound

This paper cites Efficientnet: Rethinking model scaling for con- volutional neural networks,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Efficientnet: Rethinking model scaling for con- volutional neural networks,

Reference 32

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Source-reported events for the cited work

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Observation 97cb1b04-74a3-4c25-b03f-e0aa5e84be7b · outbound

This paper cites High-performance large-scale image recognition without normalization,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference High-performance large-scale image recognition without normalization,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.579909Z

Source-reported events for the cited work

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Observation e50636b9-6167-4504-b1e4-ca2361d7bfdd · outbound

This paper cites Ia- red2: Interpretability-aware redundancy reduction for vision transform- ers,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Ia- red2: Interpretability-aware redundancy reduction for vision transform- ers,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.551094Z

Source-reported events for the cited work

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Observation d7d8b8ae-3b9a-4682-9082-11b969d85168 · outbound

This paper cites Evo-vit: Slow-fast token evolution for dynamic vision transformer,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Evo-vit: Slow-fast token evolution for dynamic vision transformer,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.525469Z

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Observation 8003ba18-028c-4aec-99ff-e69e99ef612e · outbound

This paper cites Token fusion: Bridging the gap between token pruning and token merging,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Token fusion: Bridging the gap between token pruning and token merging,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.502746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a8a5d023-6529-418a-aa39-3c600eb0082a · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 37

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Source-reported events for the cited work

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Observation 8427ab51-f64f-4175-a580-a259b68060b3 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.465685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1e7d451e-8995-4a7a-9dc5-a7b9369c9f48 · outbound

This paper cites Edge learning: The enabling technology for distributed big data analytics in the edge,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Edge learning: The enabling technology for distributed big data analytics in the edge,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.447360Z

Source-reported events for the cited work

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Observation ab0038f8-df86-4fdd-b33f-9d5a946a32a1 · outbound

This paper cites OTAS: An Elastic Transformer Serving System via Token Adaptation.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference OTAS: An Elastic Transformer Serving System via Token Adaptation

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:19:59.217652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43a9069e-a3c9-4aca-b3c6-20f5ad12c821 · outbound

This paper cites Analyzing the Structure of Attention in a Transformer Language Model.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Analyzing the Structure of Attention in a Transformer Language Model

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:59.085751Z

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Observation d0c9366d-5136-4032-9bc3-3b4c3c06da3c · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Xception: Deep learning with depthwise separable convolu- tions,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.426932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4a768149-49f9-479c-b834-657140eaa732 · outbound

This paper cites Approximation by superpositions of a sigmoidal function,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Approximation by superpositions of a sigmoidal function,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:59.095202Z

Source-reported events for the cited work

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Observation c7f1ee75-5fd2-42f4-b9ed-ac79be845217 · outbound

This paper cites Learned Thresholds Token Merging and Pruning for Vision Transformers.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference Learned Thresholds Token Merging and Pruning for Vision Transformers

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:59.099779Z

Source-reported events for the cited work

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Observation f27f4608-c54d-415f-b3ea-948f73c63830 · outbound

This paper cites PPT: Token Pruning and Pooling for Efficient Vision Transformers.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference PPT: Token Pruning and Pooling for Efficient Vision Transformers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:59.104941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baa02c17-fe57-444a-9de5-6a628a2ade34 · outbound

This paper cites No token left behind: Efficient vision transformer via dynamic token idling,.

ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference No token left behind: Efficient vision transformer via dynamic token idling,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:59.386239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.