Paper Citation Record · LEDGER

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.04523.

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

pith.paper-citation-record.v1
2506.04523 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:43:57.049767Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

35 of 35 outbound references displayed

verified exact9
verified fuzzy8
unresolved15
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 160f4c76-8534-4f7f-97f4-1a3789523361 · outbound

This paper cites Sustainable ai: Environmental implications, challenges and opportunities,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Sustainable ai: Environmental implications, challenges and opportunities,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:01.961145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c04183f-be06-48a9-83ad-89e53c52d14a · outbound

This paper cites OpenAI’s CEO Says the Age of Giant AI Models Is Already Over — wired.com,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing OpenAI’s CEO Says the Age of Giant AI Models Is Already Over — wired.com,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:01.830800Z

Source-reported events for the cited work

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Observation 440795a6-6e0e-4f50-810f-dd6744deebe6 · outbound

This paper cites Hybrid heterogeneous clusters can lower the energy consumption of llm inference workloads,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Hybrid heterogeneous clusters can lower the energy consumption of llm inference workloads,

Reference 3

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T10:44:00.460608Z

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Observation dcd3e779-e1e2-4c9d-9756-bfa91a9bd433 · outbound

This paper cites From words to watts: Benchmarking the energy costs of large language model inference,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing From words to watts: Benchmarking the energy costs of large language model inference,

Reference 4

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Observation ffd4536f-486d-4978-8318-443a03c6d202 · outbound

This paper cites The environmental impact of ai: A case study of water consumption by chat gpt,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing The environmental impact of ai: A case study of water consumption by chat gpt,

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-07T10:44:00.026748Z

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Observation 0ad21955-9fcb-488c-8b07-63792715783e · outbound

This paper cites Q&A: UW researcher discusses just how much energy ChatGPT uses — washington.edu,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Q&A: UW researcher discusses just how much energy ChatGPT uses — washington.edu,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:01.699725Z

Source-reported events for the cited work

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Observation 87540934-0347-4c0b-9985-76d1d69d98b1 · outbound

This paper cites Reservoir computing approaches to recurrent neural network training,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Reservoir computing approaches to recurrent neural network training,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:53.939335Z

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Observation af86c34b-e1cd-4652-9cd4-a4182fdf9f46 · outbound

This paper cites Reservoir Transformers.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Reservoir Transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:54.047640Z

Source-reported events for the cited work

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Observation 623f9a57-dbc1-4b9e-89e7-ae61dd4dd824 · outbound

This paper cites Analogue and physical reservoir computing using water waves: Applications in power engineering and beyond,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Analogue and physical reservoir computing using water waves: Applications in power engineering and beyond,

Reference 9

Resolution: verified exact; doi, observed 2026-08-07T10:43:59.065743Z

Source-reported events for the cited work

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Observation 3b6d5ad7-1a80-4506-b48d-d3b914a34705 · outbound

This paper cites Advances in coherent magnonics,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Advances in coherent magnonics,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:54.276501Z

Source-reported events for the cited work

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Observation 9b0f8813-67ae-403e-bbc5-601feaa96fb4 · outbound

This paper cites Hybrid quantum systems based on magnonics,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Hybrid quantum systems based on magnonics,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:54.388716Z

Source-reported events for the cited work

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Observation 28a24975-f82c-41e0-9d05-32b827a7b23b · outbound

This paper cites Towards magnonic devices based on voltage- controlled magnetic anisotropy,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Towards magnonic devices based on voltage- controlled magnetic anisotropy,

Reference 12

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Source-reported events for the cited work

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Observation 71c2814f-e560-4f4f-81ee-27547266b843 · outbound

This paper cites Magnonic crystals for data processing,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Magnonic crystals for data processing,

Reference 13

Resolution: verified exact; doi, observed 2026-08-07T10:43:58.506805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ea59ad2f-9a28-48c7-9647-918cc6d7c77c · outbound

This paper cites an unresolved cited work.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Unresolved cited work

Reference 14

Resolution: verified exact; doi, observed 2026-08-07T10:43:58.277458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 47c3f674-e67c-41a6-ae6d-a8a353b87f87 · outbound

This paper cites Nonlinear spin wave coupling in adjacent magnonic crystals,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Nonlinear spin wave coupling in adjacent magnonic crystals,

Reference 15

Resolution: verified exact; doi, observed 2026-08-07T10:43:58.076500Z

Source-reported events for the cited work

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Observation 63eeed53-1988-465b-ac2d-53f2e78a5c11 · outbound

This paper cites Nanoscale spin-wave circuits based on engineered reconfigurable spin-textures,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Nanoscale spin-wave circuits based on engineered reconfigurable spin-textures,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:54.873990Z

Source-reported events for the cited work

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Observation f2ba6c89-f214-42de-80c5-825595654766 · outbound

This paper cites Spin wave normalization toward all magnonic circuits,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Spin wave normalization toward all magnonic circuits,

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T10:43:59.715439Z

Source-reported events for the cited work

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Observation 52e07262-08e3-44c9-9085-3bc368453ed8 · outbound

This paper cites A switchable spin-wave signal splitter for magnonic networks,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing A switchable spin-wave signal splitter for magnonic networks,

Reference 18

Resolution: verified exact; doi, observed 2026-08-07T10:43:57.843424Z

Source-reported events for the cited work

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Observation bf60e2d3-bae1-4be1-a049-3540df2dab91 · outbound

This paper cites Hybrid Magnonic Reservoir Computing.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Hybrid Magnonic Reservoir Computing

Reference 19

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Source-reported events for the cited work

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Observation 44ad6dc9-98a5-4ea1-89ad-82fda4586e21 · outbound

This paper cites Iono–magnonic reservoir computing with chaotic spin wave interference manipulated by ion-gating,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Iono–magnonic reservoir computing with chaotic spin wave interference manipulated by ion-gating,

Reference 20

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Source-reported events for the cited work

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Observation e7998950-3ebd-4a3b-bf62-3552591c0574 · outbound

This paper cites On the importance of initialization and momentum in deep learning,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing On the importance of initialization and momentum in deep learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:01.581568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1c22c617-97a1-4cee-8a8d-37be86772a61 · outbound

This paper cites Understand- ing deep learning requires rethinking generalization,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Understand- ing deep learning requires rethinking generalization,

Reference 22

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Observation 1f5b9815-b9e5-457e-b1ef-65dcbbcec49c · outbound

This paper cites Variational quantum algorithms,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Variational quantum algorithms,

Reference 23

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Observation 0b396dff-153e-483c-8cb8-028aed7a7d6d · outbound

This paper cites Breast cancer wisconsin (diagnostic),.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Breast cancer wisconsin (diagnostic),

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:01.472920Z

Source-reported events for the cited work

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Observation d379919f-51ec-467a-99d5-041e19eb1762 · outbound

This paper cites Bottou,Large-Scale Machine Learning with Stochastic Gradient Descent.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Bottou,Large-Scale Machine Learning with Stochastic Gradient Descent

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:55.849661Z

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Observation ff765cc8-a6f0-4853-b75f-2baf2ec54fa4 · outbound

This paper cites Adam: A method for stochastic optimization,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Adam: A method for stochastic optimization,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:55.921838Z

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Observation e5aed5cd-ae4a-432a-b1da-c8ac75a0f692 · outbound

This paper cites Multi30k: Multilingual english-german image descriptions,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Multi30k: Multilingual english-german image descriptions,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:01.320823Z

Source-reported events for the cited work

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Observation ba6d8aaa-4f31-4bc9-9dbd-9481bda843cd · outbound

This paper cites Numerical simulations of a magnonic reservoir computer,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Numerical simulations of a magnonic reservoir computer,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:01.040995Z

Source-reported events for the cited work

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Observation dcc4f427-6ded-49ee-92df-89b4bdb959fb · outbound

This paper cites Implementing a magnonic reservoir computer model based on time-delay multiplex- ing,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Implementing a magnonic reservoir computer model based on time-delay multiplex- ing,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:56.658737Z

Source-reported events for the cited work

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Observation 644409b2-52a7-4d68-90c5-e5625236e643 · outbound

This paper cites A current- controlled magnonic reservoir for physical reservoir computing,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing A current- controlled magnonic reservoir for physical reservoir computing,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:56.793550Z

Source-reported events for the cited work

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Observation 148b1c02-c720-42b7-86f9-4f6cf155e5c5 · outbound

This paper cites Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions,

Reference 31

Resolution: verified exact; doi, observed 2026-08-07T10:43:57.298549Z

Source-reported events for the cited work

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Observation 94ba6eeb-409f-489f-a044-2e738344fe97 · outbound

This paper cites At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks,.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:44:00.646152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f2ec0d62-4049-4d33-bf31-f7e37037de5f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Adam: A Method for Stochastic Optimization

Reference 2014

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Observation a2d8f4c7-f13b-42d2-b587-51dc56281e1e · outbound

This paper cites Available: http://dx.doi.org/10.18653/v1/W16-3210.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Available: http://dx.doi.org/10.18653/v1/W16-3210

Reference 2016

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Source-reported events for the cited work

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Observation f0133127-354d-4abd-adcb-775c61855a0d · outbound

This paper cites Available: http://dx.doi.org/10.1063/5.0184848.

Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing Available: http://dx.doi.org/10.1063/5.0184848

Reference 2024

Resolution: verified exact; doi, observed 2026-08-07T10:43:57.551067Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.