Paper Citation Record · LEDGER

Leveraging Registers in Vision Transformers for Robust Adaptation

As of 22 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2501.04784.

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

pith.paper-citation-record.v1
2501.04784 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:29:37.753048Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy11
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2cdf915e-eb1c-4780-be21-6a2bd174ec6e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Leveraging Registers in Vision Transformers for Robust Adaptation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.658421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0a3b91-931a-455e-9593-cac50a13cada · outbound

This paper cites Attention is all you need.

Leveraging Registers in Vision Transformers for Robust Adaptation Attention is all you need

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.662838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e484e639-0724-4743-9fb4-5acec5407e46 · outbound

This paper cites Deit iii: Revenge of the vit.

Leveraging Registers in Vision Transformers for Robust Adaptation Deit iii: Revenge of the vit

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:38.036380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1291460b-e238-4851-9cf5-7485bc953964 · outbound

This paper cites GPT3.int8(): 8-bit matrix multiplication for transformers at scale.

Leveraging Registers in Vision Transformers for Robust Adaptation GPT3.int8(): 8-bit matrix multiplication for transformers at scale

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:38.024108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bea69a0d-e435-4a1f-8653-d36be7ac39ed · outbound

This paper cites Scalable diffusion models with transformers.

Leveraging Registers in Vision Transformers for Robust Adaptation Scalable diffusion models with transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.675944Z

Source-reported events for the cited work

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Observation 4478698e-e4ce-41b4-aa73-4bd525b93ee4 · outbound

This paper cites Vision transformers need registers.

Leveraging Registers in Vision Transformers for Robust Adaptation Vision transformers need registers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:38.003361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation efce06e7-da76-4f00-96ea-ebaf74c6f93e · outbound

This paper cites Learning useful representations for shifting tasks and distributions.

Leveraging Registers in Vision Transformers for Robust Adaptation Learning useful representations for shifting tasks and distributions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.991607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fd9d1e5c-dd2e-4bc0-8560-d2cfff5ad0e9 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Leveraging Registers in Vision Transformers for Robust Adaptation DINOv2: Learning Robust Visual Features without Supervision

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.689781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90005c22-54ec-422c-b99c-17c6be099203 · outbound

This paper cites Residual stream norms grow exponentially over the forward pass.

Leveraging Registers in Vision Transformers for Robust Adaptation Residual stream norms grow exponentially over the forward pass

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.979911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fd253dc8-33b1-4859-b74e-4ee80b2829c6 · outbound

This paper cites Bert busters: Outlier dimensions that disrupt transformers.

Leveraging Registers in Vision Transformers for Robust Adaptation Bert busters: Outlier dimensions that disrupt transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.699185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e005758e-5a3c-4b27-9ac9-67ea0c6a6644 · outbound

This paper cites Unveiling A Core Linguistic Region in Large Language Models.

Leveraging Registers in Vision Transformers for Robust Adaptation Unveiling A Core Linguistic Region in Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.703379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66348516-2039-4b13-845f-2103de31276d · outbound

This paper cites All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality.

Leveraging Registers in Vision Transformers for Robust Adaptation All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.707870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b864075-2283-4678-854d-5c93cd8eff64 · outbound

This paper cites Mamba-R: Vision Mamba ALSO Needs Registers.

Leveraging Registers in Vision Transformers for Robust Adaptation Mamba-R: Vision Mamba ALSO Needs Registers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.712482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 056d5deb-db85-4949-9ed6-c84cf0929c02 · outbound

This paper cites Understanding and Minimising Outlier Features in Neural Network Training.

Leveraging Registers in Vision Transformers for Robust Adaptation Understanding and Minimising Outlier Features in Neural Network Training

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.716745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d54ef8b7-9dd2-4ba5-a3cb-254468d55c91 · outbound

This paper cites Massive Activations in Large Language Models.

Leveraging Registers in Vision Transformers for Robust Adaptation Massive Activations in Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.720335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02ae95fa-00cb-46ac-9eac-a733ae02c01f · outbound

This paper cites Scaling vision transformers.

Leveraging Registers in Vision Transformers for Robust Adaptation Scaling vision transformers

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.959054Z

Source-reported events for the cited work

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Observation 4f014355-7ad8-4f6d-bfbb-23d6800aa710 · outbound

This paper cites An inverse scaling law for clip training.

Leveraging Registers in Vision Transformers for Robust Adaptation An inverse scaling law for clip training

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.947399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 042b6e84-df06-4f83-9ee2-6014c19da8e2 · outbound

This paper cites H-optimus-0, 2024.

Leveraging Registers in Vision Transformers for Robust Adaptation H-optimus-0, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.934191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09b1a811-0a7b-461e-a8f0-b15a5f52d4a5 · outbound

This paper cites Imagenet large scale visual recognition challenge.

Leveraging Registers in Vision Transformers for Robust Adaptation Imagenet large scale visual recognition challenge

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.735375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14db1f19-1a8e-46dd-9ec4-a2d0fcf1064e · outbound

This paper cites Natural adversarial examples.

Leveraging Registers in Vision Transformers for Robust Adaptation Natural adversarial examples

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.913728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6d6a685c-b791-4c45-b836-f9747bdfad8c · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Leveraging Registers in Vision Transformers for Robust Adaptation The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.742964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f9e7088-1d66-4b11-aa52-61d039c12202 · outbound

This paper cites Learning robust global representations by penalizing local predictive power.

Leveraging Registers in Vision Transformers for Robust Adaptation Learning robust global representations by penalizing local predictive power

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.892499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d62a89ab-a18c-41b5-ae0d-f388d4b5e965 · outbound

This paper cites Energy- based out-of-distribution detection.

Leveraging Registers in Vision Transformers for Robust Adaptation Energy- based out-of-distribution detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:29:37.878523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 47d41178-5465-48a3-a05d-5e9e81c59432 · outbound

This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks.

Leveraging Registers in Vision Transformers for Robust Adaptation A baseline for detecting misclassified and out-of-distribution examples in neural networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T21:29:37.753048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.