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Paper Citation Record · LEDGER

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2607.10677.

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

pith.paper-citation-record.v1
2607.10677 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T10:03:27.747512Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

30 of 30 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved26
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 706a60b8-3c54-4b1e-aeea-1909dcc52eb5 · outbound

This paper cites Gomez and Lukasz Kaiser and Illia Polosukhin , editor =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Gomez and Lukasz Kaiser and Illia Polosukhin , editor =

Reference 1

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:9f693638e021e616dfcf1067834e3efb94c0e3684a1cb57343ea71377086bff7

Observation 9ecbc65a-6d3a-447d-af67-96c525c9f29b · outbound

This paper cites Rethinking Attention with Performers , booktitle =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Rethinking Attention with Performers , booktitle =

Reference 2

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:185ef569e39bd6c8e062f901974d6ac414d5a10054541c9e5dfde41179f83347

Observation 76afb913-9ed5-4f7f-b663-65bd52c17f72 · outbound

This paper cites 2023 , url =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers 2023 , url =

Reference 3

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:686c56ddc8c47b0ebff85b64b783892a8809207856241e50c6b6b46983a6181c

Observation 91af7fca-44f6-4d9c-ac3b-fbef6509769c · outbound

This paper cites Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

Reference 4

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:2f33becd264390a9711fab9f99f1957ceca38512dfe87eed3517878b6a307133

Observation ea81e0e8-ca4e-4d07-8fc2-b16c30fcd782 · outbound

This paper cites On Learning the Transformer Kernel.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers On Learning the Transformer Kernel

Reference 5

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:4e4c61beb54c2314f8a7017624aeee3050a6e28be39ef9a114b89668403f85a8

Observation 076f785c-01ee-48a3-a9e2-e85a6466c8c3 · outbound

This paper cites Susskind , editor =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Susskind , editor =

Reference 6

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:14538b26125131aa5b480b5fdc4fb5d55c262f500d6a0a926afea5c268436283

Observation 38acafae-9d19-4277-9f38-f805f5deda25 · outbound

This paper cites On the Role of Attention Masks and LayerNorm in Transformers , booktitle =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers On the Role of Attention Masks and LayerNorm in Transformers , booktitle =

Reference 7

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:5f5cc8f163eccdc3772c3bb578f70433691fc9f7eb9a41021607e2bcd258f13a

Observation 58856ba4-9481-48af-ad18-84796bdab5b7 · outbound

This paper cites Hopfield Networks is All You Need , booktitle =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Hopfield Networks is All You Need , booktitle =

Reference 8

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:b93f4ae1abb5a708f962e095ac92d8458e75e3433bbbe6b206c9f1b272b58680

Observation f4238cae-6b42-469f-a231-b9b732f889f1 · outbound

This paper cites Kim , editor =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Kim , editor =

Reference 9

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:b02616f4853cef0a198f9049cb7035666bc0428c94f3b4601440642aa6fabd09

Observation 3e73f11d-6052-45a6-b564-89fc12c72b9f · outbound

This paper cites Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together , booktitle =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together , booktitle =

Reference 10

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doi, observed 2026-07-14T10:10:24.493143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:1f84aedca13e41f551a609f72ba78fa82caf3965ad520c9b5172af06b814b560

Observation 3c16a2bd-c335-4d65-8e9a-b4a029204b30 · outbound

This paper cites Communications on pure and applied mathematics , volume =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Communications on pure and applied mathematics , volume =

Reference 11

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:dd3843e7b68199bdbdc871ad789b52dd2dfc5e0d75d8506688cd2261425ce521

Observation c00cfc7a-cf08-45f6-9b0d-7965193eca8c · outbound

This paper cites Bandeira and Amit Singer and Daniel A.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Bandeira and Amit Singer and Daniel A

Reference 12

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doi, observed 2026-07-14T10:10:24.515323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:713fbb74c2ea85415212c6ccb1b91e3ce9466f9dc57532f900ee38e179667347

Observation 4488b80a-87d4-4810-9707-f602f2999bcd · outbound

This paper cites Gauge Equivariant Convolutional Networks and the Icosahedral.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Gauge Equivariant Convolutional Networks and the Icosahedral

Reference 13

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:973cad3749f7e8ba65e8886cbce8220f9d6aab5e711c2a70bd26fc4ff9a4a04f

Observation 10483729-f7e7-4942-b692-ee6de1c373c8 · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 14

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Observation 1414fed9-4a88-4331-9ac1-0da646339bf9 · outbound

This paper cites Hamilton and Vincent L.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Hamilton and Vincent L

Reference 15

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:b132aac5bc741c076be8c00e68422373c9bc8b325cf35ce0cf1119ad66fd1441

Observation 59b66704-1dc8-42ab-a951-08dfa4d61d62 · outbound

This paper cites A Framework for Non-Linear Attention via Modern Hopfield Networks.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers A Framework for Non-Linear Attention via Modern Hopfield Networks

Reference 16

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:c6b8667a4566f92542c502b56d4535448143642b07a56d5f80e9b1855e0a8828

Observation d398586e-923f-4e44-8abc-9d690e7a0594 · outbound

This paper cites Slabaugh and Stefanos Zafeiriou , title =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Slabaugh and Stefanos Zafeiriou , title =

Reference 17

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:1b2f851d3a0f732400c62b337108e46fc9578c44dc7bb11733b0fcac90e4473a

Observation 46cb9998-1f2a-4bee-9e8d-411f1bdf4f7c · outbound

This paper cites A Tensorized Transformer for Language Modeling , booktitle =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers A Tensorized Transformer for Language Modeling , booktitle =

Reference 18

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Observation bab154e8-8ef5-4076-aa55-9fa9c2fca2f7 · outbound

This paper cites Tensor Product Attention Is All You Need , journal =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Tensor Product Attention Is All You Need , journal =

Reference 19

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Observation 4368ff3f-46d2-4554-b36c-331c141bbd05 · outbound

This paper cites Towards understanding how attention mechanism works in deep learning.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Towards understanding how attention mechanism works in deep learning

Reference 20

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Observation e05fe599-061c-4a2e-8ddd-b4b459d55e57 · outbound

This paper cites On Layer Normalization in the Transformer Architecture , booktitle =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers On Layer Normalization in the Transformer Architecture , booktitle =

Reference 21

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:ed85e1e8ac854341fa4d18e642f35eea8016a315f992f27123fc386f966507ff

Observation 56c79859-8826-4a35-b97c-f43ecbd4ebbf · outbound

This paper cites Nguyen and Julian Salazar , editor =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Nguyen and Julian Salazar , editor =

Reference 22

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:263b122ef1248a390c7a4b119df50f0036f4d64db9f569a1aef3fb9ef2b7257c

Observation f8bd28a7-8a01-4075-9c14-f533239f96a4 · outbound

This paper cites Annals of Combinatorics , volume =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Annals of Combinatorics , volume =

Reference 23

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Observation 64ed1620-a306-4271-86d1-5cf4af05f912 · outbound

This paper cites Nonlinear Phenomena in Complex Systems , volume =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Nonlinear Phenomena in Complex Systems , volume =

Reference 24

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Observation 800d847e-d96c-4ddd-9b09-72bb6bd86209 · outbound

This paper cites Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,

Reference 25

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Observation 25636b4f-2b54-4010-9b46-e3c53c34b8c9 · outbound

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From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:2247e639f97fc5e461447d5b306cbf66909662a323548c808441fdecf9f0750e

Observation 9e5fa279-d767-4c06-b97c-1f8e76479bea · outbound

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From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Unresolved cited work

Reference 27

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:3f9ac57539199ff18a31be2640534d754bd33e3560346640ed00d16a59a61305

Observation d8df2033-881c-4c5b-ac16-de42431dc972 · outbound

This paper cites OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization

Reference 28

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:8e74bee9453140fef300992a51584cf34b2c1da3ea274a615532e95e15c94ba9

Observation 0e04dbdc-e22e-4456-9284-ce6aad3a70dc · outbound

This paper cites Neural Sheaf Diffusion:.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Neural Sheaf Diffusion:

Reference 29

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:58f75cb0412ee3ae839aaa7972cc21dcb1ae889cd47fae2f30a8b9e0059f0b09

Observation dbd51367-9fc0-4e10-8d82-868f4c93bd83 · outbound

This paper cites Sheaf Neural Networks with Connection Laplacians , booktitle =.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Sheaf Neural Networks with Connection Laplacians , booktitle =

Reference 30

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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:c4c5688650f292f09583571365b76c8ce598efe2108760a5daa788a8805152a3

Pith citing papers

No inbound Pith citation observations are available.