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Paper Citation Record · LEDGER

Contextually Guided Transformers via Low-Rank Adaptation

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.05672.

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

pith.paper-citation-record.v1
2506.05672 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:19:44.402023Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

29 of 29 outbound references displayed

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  • verified fuzzy10
  • unresolved17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cd0a788-6118-4b4e-9079-1f34b155f583 · outbound

This paper cites write newline.

Contextually Guided Transformers via Low-Rank Adaptation write newline

Reference 1

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Unavailable: canonical work link unavailable.

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Observation 315e0b3b-813e-49ea-b62f-0eaf4b577e9c · outbound

This paper cites Language models are few-shot learners.

Contextually Guided Transformers via Low-Rank Adaptation Language models are few-shot learners

Reference 2

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Observation af4eed04-9725-4113-9820-5ed951393386 · outbound

This paper cites Gaussian process prior variational autoencoders.

Contextually Guided Transformers via Low-Rank Adaptation Gaussian process prior variational autoencoders

Reference 3

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Observation 0c231f50-5c09-4483-b3ac-a7fa40f4076b · outbound

This paper cites Cf-vit: A general coarse-to-fine method for vision transformer.

Contextually Guided Transformers via Low-Rank Adaptation Cf-vit: A general coarse-to-fine method for vision transformer

Reference 4

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verified exact
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Observation 0aa99e17-2832-44c2-8d87-76eafffd1af2 · outbound

This paper cites Transformer-based Conditional Variational Autoencoder for Controllable Story Generation.

Contextually Guided Transformers via Low-Rank Adaptation Transformer-based Conditional Variational Autoencoder for Controllable Story Generation

Reference 5

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Observation 4a235b5b-95bb-480b-9d5f-a66b206edb56 · outbound

This paper cites In-context learning creates task vectors.

Contextually Guided Transformers via Low-Rank Adaptation In-context learning creates task vectors

Reference 6

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Observation 63781fba-02bf-41e1-b873-f9b2f1e0638d · outbound

This paper cites A VAE for Transformers with Nonparametric Variational Information Bottleneck.

Contextually Guided Transformers via Low-Rank Adaptation A VAE for Transformers with Nonparametric Variational Information Bottleneck

Reference 7

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Source-reported events for the cited work

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Observation 61c2aa74-0b6c-4bc4-a0ea-3a95bf0c4392 · outbound

This paper cites Kingma and Max Welling.

Contextually Guided Transformers via Low-Rank Adaptation Kingma and Max Welling

Reference 8

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Observation 8d683374-2425-42bc-b83c-e74de2dbd4c3 · outbound

This paper cites an unresolved cited work.

Contextually Guided Transformers via Low-Rank Adaptation Unresolved cited work

Reference 9

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Observation 304cac0f-7a19-4efe-9831-368c5ee0e1e2 · outbound

This paper cites an unresolved cited work.

Contextually Guided Transformers via Low-Rank Adaptation Unresolved cited work

Reference 10

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Observation 4948b7ce-2e7c-48ea-96ea-011431fa813a · outbound

This paper cites Fast-slow recurrent neural networks.

Contextually Guided Transformers via Low-Rank Adaptation Fast-slow recurrent neural networks

Reference 11

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Observation 349c7ef4-ffc0-4c96-b956-142df9a2cf37 · outbound

This paper cites Hypertuning: Toward adapting large language models without back-propagation.

Contextually Guided Transformers via Low-Rank Adaptation Hypertuning: Toward adapting large language models without back-propagation

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation a5c6c0be-921d-4672-bf6d-c66b3b54ff37 · outbound

This paper cites Language models are unsupervised multitask learners.

Contextually Guided Transformers via Low-Rank Adaptation Language models are unsupervised multitask learners

Reference 13

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Observation 8f7d56fa-5a80-4f47-b82a-5b79dbb8fe8b · outbound

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Contextually Guided Transformers via Low-Rank Adaptation Unresolved cited work

Reference 14

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This paper cites Orthogonal projection loss.

Contextually Guided Transformers via Low-Rank Adaptation Orthogonal projection loss

Reference 15

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Source-reported events for the cited work

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Observation 61d9ea04-d2a0-4072-b98f-25022b328446 · outbound

This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification.

Contextually Guided Transformers via Low-Rank Adaptation Dynamicvit: Efficient vision transformers with dynamic token sparsification

Reference 16

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Source-reported events for the cited work

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Observation 53123e4b-86ad-428b-9da5-97705aeff4f0 · outbound

This paper cites Learning to control fast-weight memories: An alternative to dynamic recurrent networks.

Contextually Guided Transformers via Low-Rank Adaptation Learning to control fast-weight memories: An alternative to dynamic recurrent networks

Reference 17

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Observation 66d2743d-54f1-4c0a-a61a-4f0f7957d839 · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Contextually Guided Transformers via Low-Rank Adaptation Neural Machine Translation of Rare Words with Subword Units

Reference 18

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Observation 76330345-cced-4c1b-9473-972397aa19d9 · outbound

This paper cites Patch slimming for efficient vision transformers.

Contextually Guided Transformers via Low-Rank Adaptation Patch slimming for efficient vision transformers

Reference 19

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Observation defa3c62-148b-4a2e-81dd-e3d486a3cd09 · outbound

This paper cites Visualizing data using t-sne.

Contextually Guided Transformers via Low-Rank Adaptation Visualizing data using t-sne

Reference 20

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Observation 2b166800-9dac-4a9f-9cf5-5c1b318bf6e0 · outbound

This paper cites Transformers learn in-context by gradient descent.

Contextually Guided Transformers via Low-Rank Adaptation Transformers learn in-context by gradient descent

Reference 21

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Source-reported events for the cited work

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Observation 796d962b-de31-47ab-a092-82eb208d5999 · outbound

This paper cites Uncovering mesa-optimization algorithms in transformers, 2023.

Contextually Guided Transformers via Low-Rank Adaptation Uncovering mesa-optimization algorithms in transformers, 2023

Reference 22

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Source-reported events for the cited work

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Observation a9d8e91f-2d80-474d-80f4-9815f17db886 · outbound

This paper cites T-cvae: Transformer-based conditioned variational autoencoder for story completion.

Contextually Guided Transformers via Low-Rank Adaptation T-cvae: Transformer-based conditioned variational autoencoder for story completion

Reference 23

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Observation 0b416d09-26f5-45aa-9a5b-315ccaeb6bef · outbound

This paper cites Wikimedia downloads.

Contextually Guided Transformers via Low-Rank Adaptation Wikimedia downloads

Reference 24

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Observation f9241a03-e948-4009-be8f-e8ffbd6972cf · outbound

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

Contextually Guided Transformers via Low-Rank Adaptation Evo-vit: Slow-fast token evolution for dynamic vision transformer

Reference 25

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This paper cites Chi, Jason Wei, Jeff Dean, Liam B.

Contextually Guided Transformers via Low-Rank Adaptation Chi, Jason Wei, Jeff Dean, Liam B

Reference 26

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Source-reported events for the cited work

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Observation 7349ae91-9851-462d-9a0d-61d3a535d0ee · outbound

This paper cites @esa (Ref.

Contextually Guided Transformers via Low-Rank Adaptation @esa (Ref

Reference 27

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Observation 4326401b-1090-4cd9-9d83-335cfef331e4 · outbound

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Contextually Guided Transformers via Low-Rank Adaptation Unresolved cited work

Reference 28

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Observation 1f140c7b-c20a-4b1f-82e7-c6cf0bc4e87e · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Contextually Guided Transformers via Low-Rank Adaptation Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 29

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Pith citing papers

No inbound Pith citation observations are available.