Paper Citation Record · LEDGER

What do Vision Transformers Learn? A Visual Exploration

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2212.06727.

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

pith.paper-citation-record.v1
2212.06727 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 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 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:40:32.294790Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T17:17:14.971022Z

Reference resolution

0 of 0 outbound references displayed

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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 c538e3c6-04d4-4d8a-9823-7cd63c719167 · inbound

ResidualDroppath: Enhancing Feature Reuse over Residual Connections cites this paper.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections What do Vision Transformers Learn? A Visual Exploration

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T20:40:32.294790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cce258a-b3d5-4d64-8f1f-73f2da46dd00 · inbound

TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning cites this paper.

TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning What do Vision Transformers Learn? A Visual Exploration

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T18:11:15.705533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d7426b4-84cc-4554-8330-4ebe450de04f · inbound

Causal Graphical Models for Vision-Language Compositional Understanding cites this paper.

Causal Graphical Models for Vision-Language Compositional Understanding What do Vision Transformers Learn? A Visual Exploration

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T17:10:44.328119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f05b5391-7e4d-468f-8a6c-73c5fd4584f4 · inbound

Memory Efficient Matting with Adaptive Token Routing cites this paper.

Memory Efficient Matting with Adaptive Token Routing What do Vision Transformers Learn? A Visual Exploration

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T15:47:56.053994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5bdf5b-22c8-4752-8bf7-ef7ad3bce46a · inbound

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation cites this paper.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation What do Vision Transformers Learn? A Visual Exploration

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T12:28:54.581153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a94a302-29d5-4c08-a23d-c8a26386d7f4 · inbound

Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights cites this paper.

Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights What do Vision Transformers Learn? A Visual Exploration

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T21:02:01.001570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af3d2fe9-75c3-4303-b35a-1ba8b32c0ed8 · inbound

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models cites this paper.

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models What do Vision Transformers Learn? A Visual Exploration

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T01:04:00.604755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87bc3f2b-8620-4f94-b1d9-726b1b30fdc6 · inbound

Explainability for Vision Foundation Models: A Survey cites this paper.

Explainability for Vision Foundation Models: A Survey What do Vision Transformers Learn? A Visual Exploration

Reference 253

Resolution: unresolved; no resolver link, observed 2026-08-10T17:26:35.983708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71ebff2d-17a0-4d8a-b8e2-2956bc460053 · inbound

ToSA: Token Merging with Spatial Awareness cites this paper.

ToSA: Token Merging with Spatial Awareness What do Vision Transformers Learn? A Visual Exploration

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:01:29.883551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98349d3c-4cf6-4b1e-bfa9-44ccd5c843da · inbound

Towards Distributed Neural Architectures cites this paper.

Towards Distributed Neural Architectures What do Vision Transformers Learn? A Visual Exploration

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T22:14:19.008165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86964e6e-d519-4f8d-a782-f5c8373628f6 · inbound

Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptation cites this paper.

Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptation What do Vision Transformers Learn? A Visual Exploration

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T21:52:05.620893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb8bb85-54dd-4a31-bbc4-d2298cb69104 · inbound

A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models cites this paper.

A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models What do Vision Transformers Learn? A Visual Exploration

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T01:46:14.355326Z

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.

Observation f15d0c8f-f091-4204-bd86-89d553fcb327 · inbound

Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning cites this paper.

Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning What do Vision Transformers Learn? A Visual Exploration

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:39:50.416786Z

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.

Observation 0fe62a99-234c-4b4f-a25d-e6c56e45e090 · inbound

LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift cites this paper.

LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift What do Vision Transformers Learn? A Visual Exploration

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:18:13.744646Z

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.

Observation 54c4e797-6c7b-49d6-a5d0-fce4f59bd905 · inbound

Textual Supervision Enhances Geospatial Representations in Vision-Language Models cites this paper.

Textual Supervision Enhances Geospatial Representations in Vision-Language Models What do Vision Transformers Learn? A Visual Exploration

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T17:17:14.972599Z

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.

Observation c3b693d2-8507-44b3-b09f-9dcea39679c4 · inbound

Token-Based Affordance Grounding with Large Vision-Language Models cites this paper.

Token-Based Affordance Grounding with Large Vision-Language Models What do Vision Transformers Learn? A Visual Exploration

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T01:18:51.054590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.