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Paper Citation Record · LEDGER

Scaling Laws in Linear Regression: Compute, Parameters, and Data

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.08466.

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

pith.paper-citation-record.v1
2406.08466 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:07:23.244730Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:17:30.984851Z

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 59354db8-342a-4c9e-b7bf-fd096c8a8d9a · inbound

Loss-to-Loss Prediction: Scaling Laws for All Datasets cites this paper.

Loss-to-Loss Prediction: Scaling Laws for All Datasets Scaling Laws in Linear Regression: Compute, Parameters, and Data

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-12T17:07:23.244730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T17:07:23.244730Z digest=sha256:8115d30c580ffb3c728821b5bb08153137b4d7e69349b51ab9a1aa2fcb554cc0

Observation 7db509b1-d7ca-4cfb-b347-b9c6893975e4 · 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 Laws in Linear Regression: Compute, Parameters, and Data

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:17:30.988370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-23T04:16:04.110552Z digest=sha256:6edba311e82ee301d44cdcefd28b22617c9be25be31dee4f06077b190c9e0bf3

Observation 7253aca4-cba3-480a-a09e-25da7bf8864a · inbound

Unifying Learning Dynamics and Generalization in Transformers Scaling Law cites this paper.

Unifying Learning Dynamics and Generalization in Transformers Scaling Law Scaling Laws in Linear Regression: Compute, Parameters, and Data

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-03T14:02:56.706117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:02:56.706117Z digest=sha256:4a1d5a49d412ab8cf9e101adaaf45631e829e5bde946a2b5ba9c1a7cad865ec4

Observation bbecf1f3-904c-4497-90b8-7f7debb79587 · inbound

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws cites this paper.

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws Scaling Laws in Linear Regression: Compute, Parameters, and Data

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-03T04:14:15.825611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T04:14:15.825611Z digest=sha256:dbd8c65b17093789ee9764fd7b9ffc69eb4eebc71db75a924b6b83ae562a0237

Observation ee6d3d6b-cf1f-46ad-bbec-db4b20c783f9 · inbound

Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory cites this paper.

Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory Scaling Laws in Linear Regression: Compute, Parameters, and Data

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-14T23:38:16.542266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-14T23:37:33.106390Z digest=sha256:51dcc94982eb69b4a68b1e7a00b1d88de5eb151e0af4198cd28885b966e16f5c

Observation 95b78925-6456-4d53-b9bf-c07134a3f37c · inbound

The Fourth Quadrant: A Stylized View of Benign Misfitting cites this paper.

The Fourth Quadrant: A Stylized View of Benign Misfitting Scaling Laws in Linear Regression: Compute, Parameters, and Data

Reference 121

Resolution
unresolved
no resolver link, observed 2026-08-06T00:43:15.173383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:43:15.173383Z digest=sha256:ef95f97a9ac59aadc9763b06b2f2578a43338943783718f7da70b005af67571f