Paper Citation Record · LEDGER

Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2209.13802.

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

pith.paper-citation-record.v1
2209.13802 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:55:03.151001Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T10:40:01.451420Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c4207409-16a7-4de0-a389-0ee3bb088653 · inbound

freePruner: A Training-free Approach for Large Multimodal Model Acceleration cites this paper.

freePruner: A Training-free Approach for Large Multimodal Model Acceleration Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T14:22:07.440191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 040a0f2b-e501-42b1-85f5-12aa6a7fb64c · inbound

AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning cites this paper.

AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T22:42:33.536591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa365a45-6471-41cc-8b00-a5ed8959deea · inbound

DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs cites this paper.

DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T10:55:03.151001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71ce04f2-68ba-4f82-8422-ae1bb3e22d27 · inbound

Sparsified State-Space Models are Efficient Highway Networks cites this paper.

Sparsified State-Space Models are Efficient Highway Networks Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:46.053397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 860ec1a7-aee1-403f-8680-eb4b3b6ebb18 · inbound

Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration cites this paper.

Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:06.482067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cf702b0-02e6-496d-bee0-0793b13f3f0a · inbound

Focus Through Motion: RGB-Event Collaborative Token Sparsification for Efficient Object Detection cites this paper.

Focus Through Motion: RGB-Event Collaborative Token Sparsification for Efficient Object Detection Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:40:01.454814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.