Paper Citation Record · LEDGER

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2603.17433.

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

pith.paper-citation-record.v1
2603.17433 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T23:10:44.751091Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

23 of 23 outbound references displayed

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unresolved23
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External citation measurements

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Outbound references

Observation 5ccddc60-c777-42d9-96a0-d139dca3cbb9 · outbound

This paper cites Longformer: The Long-Document Transformer.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Longformer: The Long-Document Transformer

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3d485dc-acd2-43cf-a33c-264111c15e9d · outbound

This paper cites Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 4d976685-585c-4a49-ae07-5984023b040d · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Generating Long Sequences with Sparse Transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation a9a3962f-4c36-4488-9956-2d42cde25cf1 · outbound

This paper cites Rethinking attention with performers.International Conference on Learning Representa- tions, 2021.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Rethinking attention with performers.International Conference on Learning Representa- tions, 2021

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 2f3bc301-e29d-4aad-a070-c7ce24e1f8b4 · outbound

This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher Re.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Fu, Stefano Ermon, Atri Rudra, and Christopher Re

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 73de93af-7881-4523-9a2d-75af754120ac · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.Proceedings of NAACL-HLT, pages 4171–4186, 2019.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Bert: Pre-training of deep bidirectional transformers for language understanding.Proceedings of NAACL-HLT, pages 4171–4186, 2019

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 57682505-72d6-4b57-93e6-3e982211e12d · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.Inter- national Conference on Learning Representations, 2021.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle An image is worth 16x16 words: Transformers for image recognition at scale.Inter- national Conference on Learning Representations, 2021

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Unavailable: canonical work link unavailable.

Observation b6c10395-9275-4c5a-b541-92a2f8318917 · outbound

This paper cites John Wiley & Sons, 2012.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle John Wiley & Sons, 2012

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation ff8af588-408e-4bc3-b502-81eefafc5dbe · outbound

This paper cites Training Compute-Optimal Large Language Models.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Training Compute-Optimal Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation aeea9209-3f63-497d-91be-4b788cfc72c7 · outbound

This paper cites Scaling Laws for Neural Language Models.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Scaling Laws for Neural Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation a9bf61dc-4089-4a23-a8b5-89d3833eaa84 · outbound

This paper cites FNet: Mixing Tokens with Fourier Transforms.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle FNet: Mixing Tokens with Fourier Transforms

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation c5a54752-9073-4b2a-a2f4-f2b996ff586a · outbound

This paper cites Arik, Nicolas Loeff, and Tomas Pfister.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Arik, Nicolas Loeff, and Tomas Pfister

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 7b0c726f-d323-48f7-a088-79126e7785e8 · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers.International Conference on Learning Representations, 2023.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle A time series is worth 64 words: Long-term forecasting with transformers.International Conference on Learning Representations, 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 018c33fa-193c-4b5d-8225-b4a4b8e64faf · outbound

This paper cites Information Science Reference, 2009.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Information Science Reference, 2009

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 3541a558-cbc2-4209-acec-2cf8e798a679 · outbound

This paper cites PhasorFlow: A Python Library for Unit Circle Based Computing.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle PhasorFlow: A Python Library for Unit Circle Based Computing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 515173ad-b3c8-4d05-afaf-21131d09854d · outbound

This paper cites Efficient transformers: A survey.ACM Computing Surveys, 55(6):1–28, 2022.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Efficient transformers: A survey.ACM Computing Surveys, 55(6):1–28, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation cf1c84aa-3043-4da3-9b89-31517b1d471e · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation bf1c6d31-a078-4a52-bb2b-404e73254b0a · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Linformer: Self-Attention with Linear Complexity

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Unavailable: canonical work link unavailable.

Observation 0a408b9b-a48a-4220-9bf9-e248b7521562 · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.International Conference on Learning Representations, 2023.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Timesnet: Temporal 2d-variation modeling for general time series analysis.International Conference on Learning Representations, 2023

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 337ae080-ff04-4822-a269-e3fb85d78b20 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting.Advances in Neural Information Processing Systems, 34:22419–22430, 2021.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting.Advances in Neural Information Processing Systems, 34:22419–22430, 2021

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 4f9270d8-6311-4005-b93f-a5b447e93764 · outbound

This paper cites Big bird: Transformers for longer sequences.Advances in Neural Information Processing Systems, 33:17283–17297, 2020.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Big bird: Transformers for longer sequences.Advances in Neural Information Processing Systems, 33:17283–17297, 2020

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Observation 8cc1df09-9d51-4864-a658-dbe0c82b4c1e · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Unavailable: canonical work link unavailable.

Observation 7aee3fb9-7151-434a-b0e8-03a820679bf5 · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.International Conference on Machine Learning, pages 27268–27286, 2022.

The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.International Conference on Machine Learning, pages 27268–27286, 2022

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T23:10:44.751091Z

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Pith citing papers

No inbound Pith citation observations are available.