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Paper Citation Record · LEDGER

Artificial Kuramoto Oscillatory Neurons

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2410.13821.

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

pith.paper-citation-record.v1
2410.13821 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:02:37.533083Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T20:30:07.755382Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0130047b-1918-4095-9f25-e47536c79e1b · inbound

Traveling Waves Integrate Spatial Information Through Time cites this paper.

Traveling Waves Integrate Spatial Information Through Time Artificial Kuramoto Oscillatory Neurons

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-08T17:02:37.533083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:02:37.533083Z digest=sha256:9df39f1c3a89a760fee197af89e4fa139eb19edce090af72657cfb5fbd5fa69c

Observation 3cdbbfdf-d794-4404-853d-a58c58f47c57 · inbound

Sudoku-Bench: Evaluating creative reasoning with Sudoku variants cites this paper.

Sudoku-Bench: Evaluating creative reasoning with Sudoku variants Artificial Kuramoto Oscillatory Neurons

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-07T15:08:29.413797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:08:29.413797Z digest=sha256:07ae2f43692266e97ecb60d043ef68577902f0b43d114ce3d57e72e44b492ec0

Observation ecb7413e-e86e-4876-bf1b-7dbec71b3a93 · inbound

GASPnet: Global Agreement to Synchronize Phases cites this paper.

GASPnet: Global Agreement to Synchronize Phases Artificial Kuramoto Oscillatory Neurons

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T15:09:39.156194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:09:39.156194Z digest=sha256:516843d38f6b0cbaf1882f74fbde5b36d7821553df3b00906a7ed21f3e3262a8

Observation dcf18fa7-c4c5-410d-a1da-f749edb87630 · inbound

Solving Sudoku using oscillatory neural networks cites this paper.

Solving Sudoku using oscillatory neural networks Artificial Kuramoto Oscillatory Neurons

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T05:11:11.841754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:11:11.841754Z digest=sha256:a805d668c4d5daacfc1f269b5ca9be7be1b1f4a5add85560b70f622fab96fe3d

Observation 0db284a9-7607-4770-973d-f50dd699b081 · inbound

Designing learning in high dimensional oscillator networks with low dimensional read-out cites this paper.

Designing learning in high dimensional oscillator networks with low dimensional read-out Artificial Kuramoto Oscillatory Neurons

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T13:17:42.993966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:17:42.993966Z digest=sha256:1833b481a2f85d5704bcbd9b779649b44f7200b280d274336e784852dc8a733c

Observation 5fa72caf-ae88-4fa8-8bbb-89475b36b3e4 · inbound

Global synchronization beyond dense graphs: the case of threshold graphs cites this paper.

Global synchronization beyond dense graphs: the case of threshold graphs Artificial Kuramoto Oscillatory Neurons

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-03T22:08:28.545922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T22:08:28.545922Z digest=sha256:a03821a2393f7ed3f3b557514d5c5618610d750f3d3a6655757de68df6637f81

Observation cadcdfd3-21d3-423a-99ca-23756e1cc188 · inbound

Understanding LoRA as Knowledge Memory: An Empirical Analysis cites this paper.

Understanding LoRA as Knowledge Memory: An Empirical Analysis Artificial Kuramoto Oscillatory Neurons

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-15T18:10:13.225886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T18:09:54.899039Z digest=sha256:619e183796bea4843852ca09894ee708e6ecf03a0b8fefaa60cfe213cec93ee2

Observation 64244864-9bbd-4d50-86d9-bedc58493c40 · inbound

Understanding LoRA as Knowledge Memory: An Empirical Analysis cites this paper.

Understanding LoRA as Knowledge Memory: An Empirical Analysis Artificial Kuramoto Oscillatory Neurons

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-02T19:49:45.359086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:49:45.359086Z digest=sha256:273771d7ba0ee65a89aeab7fce396d648dee59597322e5ba3e628a39eff80bbe

Observation 92554105-fa67-4b7c-8d74-b989a3c615bf · inbound

An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modeling cites this paper.

An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modeling Artificial Kuramoto Oscillatory Neurons

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-09T22:44:14.909373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-09T22:42:10.197470Z digest=sha256:ae55083632ae30ee7cf308169315e0dc645daa195c55ea715df40e12972ffe49

Observation 6c3cb6b5-e020-4d53-8d77-4a8c21c5713e · inbound

Demystifying Manifold Constraints in LLM Pre-training cites this paper.

Demystifying Manifold Constraints in LLM Pre-training Artificial Kuramoto Oscillatory Neurons

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:16:08.449352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T17:44:44.438637Z digest=sha256:33af90f2b4cf1460093d4ca49302058db8630e748c9a7760566c116db8becbd1

Observation adde4de4-76e5-442c-b7c5-8930d700cefa · inbound

Spontaneous symmetry breaking and Goldstone modes for deep information propagation cites this paper.

Spontaneous symmetry breaking and Goldstone modes for deep information propagation Artificial Kuramoto Oscillatory Neurons

Reference 13

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T14:25:46.905903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T21:19:34.927278Z digest=sha256:81173036aaecaa436c7cf7bf51ef47bd0e157923f54cb3e76f7467916101b1d3

Observation 510c7893-b9c3-46c6-91b3-5f7b7b424698 · inbound

Exact expression for maximum Lyapunov exponent during transients in computationally powerful dynamical networks cites this paper.

Exact expression for maximum Lyapunov exponent during transients in computationally powerful dynamical networks Artificial Kuramoto Oscillatory Neurons

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-21T01:19:20.821439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T01:17:53.114105Z digest=sha256:a231dec8d4e5ed7ca24a7020cdc3fbdab736cd715696ea3a638624052e29b460

Observation 06805f9b-99d4-4cab-9f27-a857adb5634b · inbound

Formalizing the Binding Problem cites this paper.

Formalizing the Binding Problem Artificial Kuramoto Oscillatory Neurons

Reference 80

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T03:06:29.823128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T10:21:50.279366Z digest=sha256:083c151ce79f4338803b26e70d283c41c180ffb0967d7dbf328178178940c55f

Observation 38f1eefc-1cc3-497e-bd45-0a0519159b32 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Artificial Kuramoto Oscillatory Neurons

Reference 135

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T20:30:07.757045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-25T20:05:09.179627Z digest=sha256:f3c2fc210a9f504030fac0f6bf95cdde4f4fac2c434884c53cef819c6740f21f

Observation a7cbeb80-fb3c-40f0-86b1-dee640f15e80 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Artificial Kuramoto Oscillatory Neurons

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-02T10:14:09.825720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T10:14:09.825720Z digest=sha256:89ab3e3721cd4885cae92e5b467e60ebcc8d6209910c061bede23bf96f7030fe

Observation 5c68fb96-85ae-4775-9c0f-f9aaa44598f8 · inbound

Generative Models on Analog Hardware with Dynamics cites this paper.

Generative Models on Analog Hardware with Dynamics Artificial Kuramoto Oscillatory Neurons

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-07-04T15:49:56.476034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T01:25:30.800640Z digest=sha256:5de0aa99d36f5906425c55ad8c6e9acecf99f315d3748b5c3daeaba34a7e4652

Observation 03dc02da-47f2-441f-a5cd-5d865cd9e5dd · inbound

Graph Coloring Approach to Solving Sudoku with Oscillatory Neural Networks cites this paper.

Graph Coloring Approach to Solving Sudoku with Oscillatory Neural Networks Artificial Kuramoto Oscillatory Neurons

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-01T22:21:29.595357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T22:21:29.595357Z digest=sha256:4028f21487777c8fdf3bcc9822d9d25372c6a6f450392f600a45c0e208c24568