Paper Citation Record · LEDGER

Scaling Exponents Across Parameterizations and Optimizers

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2407.05872.

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

pith.paper-citation-record.v1
2407.05872 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:21:20.377243Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 28c08736-595d-4735-9602-dae2f7e9964a · inbound

GWT: Scalable Optimizer State Compression for Large Language Model Training cites this paper.

GWT: Scalable Optimizer State Compression for Large Language Model Training Scaling Exponents Across Parameterizations and Optimizers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-23T05:57:36.655891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b70c9695-3fb7-44af-abc1-b35ac67cf3bb · inbound

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

Avoiding spurious sharpness minimization broadens applicability of SAM Scaling Exponents Across Parameterizations and Optimizers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T12:21:20.377243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7e52749-50eb-49f8-8199-6388b3bf013b · inbound

Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer cites this paper.

Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer Scaling Exponents Across Parameterizations and Optimizers

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:36.874008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54ecae26-47be-45b3-96f0-58a71145a502 · inbound

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models cites this paper.

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models Scaling Exponents Across Parameterizations and Optimizers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:17:30.972203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2d965bc0-1928-4efa-a0e7-9ba8e7c6afd4 · inbound

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach cites this paper.

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach Scaling Exponents Across Parameterizations and Optimizers

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T15:39:41.136195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f225e47c-d49a-40d2-8c6e-9d2916046295 · inbound

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning cites this paper.

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning Scaling Exponents Across Parameterizations and Optimizers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:31:20.364789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0231a66f-5898-4deb-a78f-3976de64ab0d · inbound

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks cites this paper.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Scaling Exponents Across Parameterizations and Optimizers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T20:48:50.834417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0385b0d-7ebe-4c00-8d84-3431dec3bbdc · inbound

Decoupled Relative Learning Rate Schedules cites this paper.

Decoupled Relative Learning Rate Schedules Scaling Exponents Across Parameterizations and Optimizers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:11.824040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1af5ec08-b7dd-43b3-acae-d35d9369cca7 · inbound

Feature learning is decoupled from generalization in high capacity neural networks cites this paper.

Feature learning is decoupled from generalization in high capacity neural networks Scaling Exponents Across Parameterizations and Optimizers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T14:17:37.081920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f93461aa-05fe-4da9-adf6-69f66ffbcde1 · inbound

Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model cites this paper.

Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model Scaling Exponents Across Parameterizations and Optimizers

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T07:00:43.299777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9102af60-12fc-4a52-b523-61953e1188a0 · inbound

Parcae: Scaling Laws For Stable Looped Language Models cites this paper.

Parcae: Scaling Laws For Stable Looped Language Models Scaling Exponents Across Parameterizations and Optimizers

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:21:01.021466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a223976d-c983-44ac-85da-6472e97be7d7 · inbound

C-voting: Confidence-Based Test-Time Voting without Explicit Energy Functions cites this paper.

C-voting: Confidence-Based Test-Time Voting without Explicit Energy Functions Scaling Exponents Across Parameterizations and Optimizers

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T13:05:24.996003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 805efe36-d7e0-4353-96ba-7b0aba39edc1 · inbound

Learning Rate Transfer in Normalized Transformers cites this paper.

Learning Rate Transfer in Normalized Transformers Scaling Exponents Across Parameterizations and Optimizers

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:51:28.083967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e55164d-4c29-441a-a4e6-ec14cbc6e6b6 · inbound

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data Scaling Exponents Across Parameterizations and Optimizers

Reference 141

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:57:21.512477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d85931f2-99b1-4294-9198-7a845be45fa1 · inbound

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization cites this paper.

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization Scaling Exponents Across Parameterizations and Optimizers

Reference 79

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:49:44.837389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fa8b0839-ae38-430c-8d99-827db6b9e8fc · inbound

GQA-{\mu}P: The maximal parameterization update for grouped query attention cites this paper.

GQA-{\mu}P: The maximal parameterization update for grouped query attention Scaling Exponents Across Parameterizations and Optimizers

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T16:37:39.842088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 08b3d673-2af3-4b48-a3b2-09dd84cd135e · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Scaling Exponents Across Parameterizations and Optimizers

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T15:39:33.195350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0848018-1c61-4497-a063-900a48ac9109 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Scaling Exponents Across Parameterizations and Optimizers

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T21:57:25.317838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38c7f7b7-d4c8-4b42-8683-bb38aed6a0d7 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Scaling Exponents Across Parameterizations and Optimizers

Reference 168

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:30:07.676652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 44235205-ee6f-4780-be28-54aab9ef7635 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Scaling Exponents Across Parameterizations and Optimizers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T10:14:06.198299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ca474ff-08db-4668-8d38-4b65e51d4f23 · inbound

SOAP, Muon, and Beyond: Pushing LLM Pretraining Scales cites this paper.

SOAP, Muon, and Beyond: Pushing LLM Pretraining Scales Scaling Exponents Across Parameterizations and Optimizers

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-02T06:45:17.238514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.