Paper Citation Record · LEDGER

Worker Disagreement Reveals Sharp Directions in Local SGD

As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2605.27739.

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

pith.paper-citation-record.v1
2605.27739 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T18:15:03.796618Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

20 of 20 outbound references displayed

verified exact9
verified fuzzy0
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 492fe671-9365-422e-b59e-acd0a519aae0 · outbound

This paper cites High- dimensional sgd aligns with emerging outlier eigenspaces.

Worker Disagreement Reveals Sharp Directions in Local SGD High- dimensional sgd aligns with emerging outlier eigenspaces

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f74cf5c-cc2e-423c-b012-b00b3249beb8 · outbound

This paper cites An investigation into neural net opti- mization via hessian eigenvalue density.

Worker Disagreement Reveals Sharp Directions in Local SGD An investigation into neural net opti- mization via hessian eigenvalue density

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1de44867-f33f-4df6-9a1d-b82a143ad094 · outbound

This paper cites Gradient Descent Happens in a Tiny Subspace.

Worker Disagreement Reveals Sharp Directions in Local SGD Gradient Descent Happens in a Tiny Subspace

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-06-29T18:23:51.067374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9146308-07c7-455a-8f54-52940db5cccb · outbound

This paper cites an unresolved cited work.

Worker Disagreement Reveals Sharp Directions in Local SGD Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8fa8932-0965-4331-b47c-364619ccf519 · outbound

This paper cites Learning multiple layers of features from tiny images.

Worker Disagreement Reveals Sharp Directions in Local SGD Learning multiple layers of features from tiny images

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 091c2c92-0375-43b4-935b-6a1833e52b0d · outbound

This paper cites Gradient-based learning applied to document recognition.

Worker Disagreement Reveals Sharp Directions in Local SGD Gradient-based learning applied to document recognition

Reference 6

Resolution: verified exact; doi, observed 2026-06-29T18:23:50.276480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 85f755e1-f3e7-41a0-bfad-cd9e3260c89c · outbound

This paper cites Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3411–3420, 2023.

Worker Disagreement Reveals Sharp Directions in Local SGD Low dimensional trajectory hypothesis is true: Dnns can be trained in tiny subspaces.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3411–3420, 2023

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:23:50.281807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd12b2b7-8885-4367-9b2f-1c730f1c1b17 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Worker Disagreement Reveals Sharp Directions in Local SGD Communication-efficient learning of deep networks from decentralized data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12ec6ffd-fc2b-4c41-b34f-e434f2330d0d · outbound

This paper cites The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size.

Worker Disagreement Reveals Sharp Directions in Local SGD The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-06-29T18:23:51.069932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c00f88d1-d4f6-46e6-940a-efa06113df5a · outbound

This paper cites Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond.

Worker Disagreement Reveals Sharp Directions in Local SGD Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-06-29T18:23:51.072393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f5e39b8a-fd6a-4bbf-81df-4f048571de40 · outbound

This paper cites Empirical Analysis of the Hessian of Over-Parametrized Neural Networks.

Worker Disagreement Reveals Sharp Directions in Local SGD Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-06-29T18:23:51.075039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4513a333-24f2-49d5-9d9a-7b588705c611 · outbound

This paper cites Recursive deep models for semantic compositionality over a sen- timent treebank.

Worker Disagreement Reveals Sharp Directions in Local SGD Recursive deep models for semantic compositionality over a sen- timent treebank

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da05f4f5-9d0b-4e60-96b6-f6b67796df17 · outbound

This paper cites Does sgd really happen in tiny subspaces? InInternational Conference on Learning Representations, volume 2025, pages 8086–8120, 2025.

Worker Disagreement Reveals Sharp Directions in Local SGD Does sgd really happen in tiny subspaces? InInternational Conference on Learning Representations, volume 2025, pages 8086–8120, 2025

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50b89329-a6a1-4281-84f8-963b54eacda8 · outbound

This paper cites Local sgd converges fast and communicates little.

Worker Disagreement Reveals Sharp Directions in Local SGD Local sgd converges fast and communicates little

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad5c5988-25cc-4130-af6c-5ba4c6095489 · outbound

This paper cites Investigating the overlooked hessian structure: From CNNs to LLMs.

Worker Disagreement Reveals Sharp Directions in Local SGD Investigating the overlooked hessian structure: From CNNs to LLMs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8411df8-3b3e-4013-b567-571826b76f0e · outbound

This paper cites On the overlooked struc- ture of stochastic gradients.

Worker Disagreement Reveals Sharp Directions in Local SGD On the overlooked struc- ture of stochastic gradients

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6035fb1-18f8-4b2d-8cff-9141302e92e1 · outbound

This paper cites Compressible dynamics in deep overpa- rameterized low-rank learning and adaptation.

Worker Disagreement Reveals Sharp Directions in Local SGD Compressible dynamics in deep overpa- rameterized low-rank learning and adaptation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T18:15:03.796618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1918ce67-fa02-4a2f-b3dd-3d4d53d2d41c · outbound

This paper cites On the Superlinear Relationship between SGD Noise Covariance and Loss Landscape Curvature.

Worker Disagreement Reveals Sharp Directions in Local SGD On the Superlinear Relationship between SGD Noise Covariance and Loss Landscape Curvature

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-06-29T18:23:51.077809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 28bcc95c-cc92-4a61-8760-099982b453ca · outbound

This paper cites BSFA: Leveraging the sub- space dichotomy to accelerate neural network training.

Worker Disagreement Reveals Sharp Directions in Local SGD BSFA: Leveraging the sub- space dichotomy to accelerate neural network training

Reference 19

Resolution: verified exact; doi, observed 2026-06-29T18:23:50.278813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 51196737-5b18-470d-96d2-0e380a4e5ba4 · outbound

This paper cites The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects.

Worker Disagreement Reveals Sharp Directions in Local SGD The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-06-29T18:23:51.064929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.