Paper Citation Record · LEDGER

Toward Understanding Why Adam Converges Faster Than SGD for Transformers

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2306.00204.

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

pith.paper-citation-record.v1
2306.00204 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:55:18.115103Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:27:30.098265Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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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 5d3d1554-d265-4b76-8d1e-8be221b1f8a0 · inbound

Revisiting the Initial Steps in Adaptive Gradient Descent Optimization cites this paper.

Revisiting the Initial Steps in Adaptive Gradient Descent Optimization Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T23:55:18.115103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b515489-1a8d-484b-9237-153d3ac5cfce · inbound

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond cites this paper.

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-11T20:09:35.207510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de7bce79-ad55-4a69-8474-051b04a9546c · inbound

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism cites this paper.

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T23:08:28.222610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 443d15c7-ca51-4408-bf6b-4c2bf2a238e3 · inbound

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field cites this paper.

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 174

Resolution: unresolved; no resolver link, observed 2026-08-10T18:10:55.058541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35b501fc-e92c-452e-9964-c7f63f063938 · inbound

Avoiding spurious sharpness minimization broadens applicability of SAM cites this paper.

Avoiding spurious sharpness minimization broadens applicability of SAM Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T12:21:20.501096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86065612-bebb-4c33-b7ee-6d21d2f4474e · inbound

Mechanistic Insights into Grokking from the Embedding Layer cites this paper.

Mechanistic Insights into Grokking from the Embedding Layer Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:20:32.846977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c90daf0d-ba2c-4c9c-8d1c-cfba25a472cb · inbound

Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling cites this paper.

Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:30.615997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94da035a-eaff-4fde-bff5-c5e39899b738 · inbound

Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails cites this paper.

Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:36:17.446775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a94a8ef7-bd88-443b-ab9a-14d63ed5e166 · inbound

Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails cites this paper.

Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T11:50:03.763820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 23181093-ef2c-42ff-804d-ff41c271be07 · inbound

Spatial navigation in preclinical Alzheimer's disease: A review cites this paper.

Spatial navigation in preclinical Alzheimer's disease: A review Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-13T19:53:53.219607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f02d5820-7681-4c60-b805-390ab01bc326 · inbound

Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent cites this paper.

Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:16:09.672347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fbdbb427-e811-45ef-b2ed-c306509e69e1 · inbound

Convergence of difference inclusions via a diameter criterion cites this paper.

Convergence of difference inclusions via a diameter criterion Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 118

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:23:31.750140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0ca5b382-defb-4f32-aac0-d706b0f72e82 · inbound

Revisiting the Adam-SGD Gap in LLM Pre-Training: The Role of Large Effective Learning Rates cites this paper.

Revisiting the Adam-SGD Gap in LLM Pre-Training: The Role of Large Effective Learning Rates Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:38:19.452751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29350e22-a680-4dad-b17d-d3242da6f806 · inbound

Looped Transformers with Layer Normalization Provably Learn the Power Method cites this paper.

Looped Transformers with Layer Normalization Provably Learn the Power Method Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:22:35.276216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f1ec825-57ee-4075-b27d-5c225b58b0a2 · inbound

Why Muon Outperforms Adam: A Curvature Perspective cites this paper.

Why Muon Outperforms Adam: A Curvature Perspective Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 174

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:16:44.460115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c0eb6d77-ba4d-4e16-be4b-ff2dc4a299bb · inbound

Muon Learns More Robust and Transferable Features than Adam cites this paper.

Muon Learns More Robust and Transferable Features than Adam Toward Understanding Why Adam Converges Faster Than SGD for Transformers

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:27:30.099527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.