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Paper Citation Record · LEDGER

Normalized Gradients for All

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2308.05621.

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

pith.paper-citation-record.v1
2308.05621 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:10.173055Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T14:04:45.249124Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 b86d2590-5808-45dc-b823-0f3bbfcefae2 · inbound

Training Deep Learning Models with Norm-Constrained LMOs cites this paper.

Training Deep Learning Models with Norm-Constrained LMOs Normalized Gradients for All

Reference 201

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T21:22:37.077202Z

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.

source=arxiv_source observed=2026-05-21T21:22:36.870292Z digest=sha256:1782e176a932cbe6f7b82bbff1f57f872e285db33645cb02e4092826afba6b2e

Observation 9bb9c299-cac0-4b00-a284-ab1de3f4a0da · inbound

Glocal Smoothness: Line search and adaptive step sizes can help in theory too! cites this paper.

Glocal Smoothness: Line search and adaptive step sizes can help in theory too! Normalized Gradients for All

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-22T01:00:52.243994Z

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.

source=pdf_text observed=2026-05-22T00:57:03.982703Z digest=sha256:c415b047b2396907e487b77f4a5261231ee390f03e1505896c683ae7ef2b5dbc

Observation 697105f1-47ad-4c49-84b3-236b98b37cd2 · inbound

SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration cites this paper.

SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration Normalized Gradients for All

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T21:45:10.173055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:45:10.173055Z digest=sha256:55034dcef3a210c394e495a55c9476fb4093077e49127bec02448e94548fb456

Observation b294c62b-46ac-4af6-8ee9-5afcaa70cf19 · inbound

Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization cites this paper.

Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization Normalized Gradients for All

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T17:59:08.087181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:59:08.087181Z digest=sha256:2069cfdefa28738bc82c187ed71083e0f5503568d792f4195b3c5ecd6535b98d

Observation 11e49d20-8ae1-448d-863e-01dd7de3ab65 · inbound

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

AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates Normalized Gradients for All

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T11:25:45.035920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:25:45.035920Z digest=sha256:2a04dd51d35e427548902101e2f4f393ad94e943ae54898a1f33a7f1395ef386

Observation 5938504b-6833-4ef0-89df-25d93ece93bc · inbound

Optimal Projection-Free Adaptive SGD for Matrix Optimization cites this paper.

Optimal Projection-Free Adaptive SGD for Matrix Optimization Normalized Gradients for All

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T20:58:15.730379Z

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.

source=pdf_text observed=2026-05-13T20:56:41.466669Z digest=sha256:cba07b489429c511278cedab9c183c878165fdf6f60e44542de78a9393ead315

Observation 262390be-1b2b-4bd2-bd3c-461b3dfb44b2 · inbound

Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates cites this paper.

Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates Normalized Gradients for All

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T00:41:05.436600Z

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.

source=pdf_text observed=2026-05-10T18:23:59.771222Z digest=sha256:f7c0d85b59d4e91eaab8d533a4ce7572909ed2a4d33f1765e0ccb37d40482d45

Observation 5e3d897c-ec10-4a8d-bafa-aa34785c4541 · inbound

Function-free Optimization via Comparison Oracles cites this paper.

Function-free Optimization via Comparison Oracles Normalized Gradients for All

Reference 29

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T00:09:16.827801Z

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.

source=pdf_text observed=2026-05-21T00:08:34.431809Z digest=sha256:88cc245c014c484dc1bef804c8fe471207b4e3d1b7d56c3aa8def2c44d9336b9

Observation 9b1d2338-fcbc-4e2b-98a2-94d06fb65a26 · inbound

AdaGrad does not adapt to H\"older-smoothness for composite objectives cites this paper.

AdaGrad does not adapt to H\"older-smoothness for composite objectives Normalized Gradients for All

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T14:04:45.251069Z

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.

source=arxiv_source observed=2026-06-30T05:37:19.393934Z digest=sha256:bafdedaa47e9a8d22a5bd3cee0995d6822eb79b175354400fbb027b84c9df6d7