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Paper Citation Record · LEDGER

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:1808.05671.

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

pith.paper-citation-record.v1
1808.05671 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:55:18.218036Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:18:56.227441Z

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 bccefbbd-be4d-4c98-8b61-1ba210c4253f · inbound

Adaptive Federated Optimization cites this paper.

Adaptive Federated Optimization On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 196

Resolution
verified exact
arxiv_id, observed 2026-05-21T10:30:58.754320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-21T10:30:58.601351Z digest=sha256:4b9e257a7cba81bbad95f6a43eed05edd4b620ab06f9d7df9a820e3182a49952

Observation aa438afe-3957-41d3-a43e-215acaba8e28 · inbound

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

Revisiting the Initial Steps in Adaptive Gradient Descent Optimization On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T23:55:18.218036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:55:18.218036Z digest=sha256:5c8d6a635615b1f5967b43de0cb54e595b05705dcfcfaed4db68d66b7b52eb19

Observation 0623f153-eed2-4dce-8f30-f3a365d15da1 · inbound

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods cites this paper.

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-11T00:46:12.809159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:46:12.809159Z digest=sha256:0747ff2bd2e14f9476f81360b954b2e745ab500680953b1960b2410589448b3f

Observation 08d1e229-8927-4b87-8566-90672bbda644 · inbound

Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization cites this paper.

Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T13:47:36.321431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:47:36.321431Z digest=sha256:b7f63a2c740e445483990f57565e5e7987e4dcda5e7fc50341a246891e2ccd7f

Observation 3f164c29-be32-4d24-9a0b-52ed3180529c · inbound

LightSAM: Parameter-Agnostic Sharpness-Aware Minimization cites this paper.

LightSAM: Parameter-Agnostic Sharpness-Aware Minimization On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T12:32:07.720112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:32:07.720112Z digest=sha256:da0c4ea3abe1a89619f9ce0e6c65d442912dff502610acfd8b08ae8b0f152f10

Observation 87c234ca-22bf-4fd3-9933-f7575c503978 · inbound

Unified Scaling Laws for Compressed Representations cites this paper.

Unified Scaling Laws for Compressed Representations On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:11.956311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:40:11.956311Z digest=sha256:754d000c385dce61f2ef7248859c6a14dbc05fac0aa433fc001d2b8b64d1b3fe

Observation 450a164b-d7cd-4752-8a10-00037206b6d5 · inbound

AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates cites this paper.

AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T11:25:45.073914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:25:45.073914Z digest=sha256:deabec4d86ffe8f21fc11bc47d874014e24a3bb8f3f58713d1e3882ba8fe590f

Observation d3816d0f-3c14-4be1-a3be-b3d9ca3484b9 · inbound

On the Convergence of Muon and Beyond cites this paper.

On the Convergence of Muon and Beyond On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 54

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T15:56:34.125799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-18T15:56:30.602824Z digest=sha256:985fe07c1cc182e6d562a3081ea7a07201c3c9b6bd032afe7a35157537a79750

Observation 99e3d395-1878-45e9-9a30-e3f728ead727 · inbound

Deterministic Adam-Inspired Methods with Accelerated Convergence Rate cites this paper.

Deterministic Adam-Inspired Methods with Accelerated Convergence Rate On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:16:00.264734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T16:45:24.402328Z digest=sha256:08662f5b42c3a636c0c26ac04e8c0de39d7ebe8e7a53f7fc1fb3d0b91fec6b32

Observation 2a3d186c-4a3d-418c-a054-8b6a9f3313a5 · inbound

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

Why Muon Outperforms Adam: A Curvature Perspective On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 200

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:06:44.983118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-28T07:04:21.012269Z digest=sha256:13eba44f043e0912a978f64a41c58279980601c27d40ea2a40aa2ac7c5a36712

Observation ec4e2980-1b78-422f-abd2-a03388884ca0 · inbound

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

Muon Learns More Robust and Transferable Features than Adam On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-07-03T00:27:30.171233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-27T17:08:30.717799Z digest=sha256:d303b4b33790b6358888e0bde84bcf95db5eb7993a14364961219ce277d979df

Observation 932d527f-7689-4bfb-bd0d-c7d83dabc482 · inbound

MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization cites this paper.

MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 28

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T20:18:56.228895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-27T01:31:53.550342Z digest=sha256:e958b77f3f80a8101d384501aa9ce17b21a2ce75a66288c3147e0605f25b4bd7

Observation 1b01ec68-af3b-433f-9650-6f3a773a9610 · inbound

FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity cites this paper.

FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Reference 94

Resolution
unresolved
no resolver link, observed 2026-07-12T00:07:55.485589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T00:07:55.485589Z digest=sha256:4af2d032db76f16204ab0bf58c660f42dc50f6702417bda88582aeba4fe30ac0