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Paper Citation Record · LEDGER

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

As of 23 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 2026-08-23T06:30:58.430688+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=sha256:8dac16746f21e27255b588e9034fd1925f3785deb15d2c59b01b61a0c0f1fa93

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=sha256:4d07e2c0d5b7b14882d27793d0f1a336b3a5369c7902997036928f7afed25234

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=sha256:8c9a90478592ddc2feeeabd50e59b32ea744de0e2f0e12e4c8b5ff2cf5b60e51

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=sha256:9b0a606bb1c888fbef5581824bcf567612f855ad9872917214ba20de7fe21557

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=sha256:18f3652849dbcfe0f0d926dab03fa1b769a8ad981244a8e6849988b639d72777

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:de6f2fa9e18c69ba76b35bfed285d5013fb9c8ad59813e1941f8f34505e6f726

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=sha256:85a881c4dafdd241c834c6df6adfc452a8b357e443a58755120cb28d90d7b8c3

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=sha256:93dd8768accd72b30adb62c0689a58825a6a1173aac87c1992926c9eff6fc51d

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=sha256:585bd33ce104a3744339a9f405c072f25e36593909fa38bf69c2e0f6e5d985a9

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:b34c1257c71cca39af7764e20e86339a8ace37be071ab0027d6ae7f50fe1f289

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=sha256:85bcb3345da695d05baec5634c022df655789239f132274a4a8f972ca8c6f143

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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verified exact
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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:19906c4a8c031c7d7263b21c1bb8a82c653af25618b179549ef07ea641a8529c

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=sha256:787883ba6ca7f4c5242ae99a010499a28f248f6c04754a6ab1d6ca9d21ea9962

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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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:166bdd3073c65207accf23874f221f5f1c50dd359bd8b4b2dd0abbb7d38fd195

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:f8d8bcc9c6ef3659ed2024c473cce226bc7753e4ae346b83f6530cca61af83b9

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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local_arxiv, observed 2026-07-14T10:10:24.509576Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:bce88ca96844cfa2f5c11d12681d6b618c26eab30c7b4bd981de299513adcb7e

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:c547d14e59a3a43b0919630e6be1f215988f31d49c37e48821bf4f44af1ce7f3

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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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:3262080fbde4002e782b7148640f3c2937567c0619f20bc5907803b99d208006

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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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:a05294da7afaccdd13b1568a5856ac214533f85087df744bf114d3f4291caeb5

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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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:7907eafda3e802b159ab1b695914b6a5733e6edd118952d6b9f18ccf890535ba

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=sha256:99584b55ba13fe2d86d1b05c4b78e930d04b1dd5e19e90da110b6114eee2286f

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=sha256:949bd4d4574157ae0ac94563c606f0264ee2976a0a9af67f80eef34a06bd9103

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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source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:f4f6a3bd22a1643637b78e251fef6b0a6d45292b82ea25cd4760a05005b5a013

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-07-14T10:03:27.747512Z digest=sha256:f00e5ec95e345055c2e84053555b332035f7d19341444bfa676b5ac1f895d416

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=sha256:9ee06fe3b12dc8882c501346f9dfee90a4a02034ae130c1e2a82f9064bf1c8be

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:a68958a9596b2b15cf151649abc71d8fa80996943ac31c2d994d22dd0c8e602c

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=sha256:992ddda446455955fd03da5caad196ba9e040cb945f9d1387fa295466c11467a

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=sha256:71d4013f4f2ab0691faaed887f52115a7f8851f87be8520bd77f941f310a1176

Pith citing papers

No inbound Pith citation observations are available.