Paper Citation Record · LEDGER

How Feature Learning Can Improve Neural Scaling Laws

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2409.17858.

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

pith.paper-citation-record.v1
2409.17858 v2

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 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:07:23.162798Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1e63f601-a027-4d9b-8dec-c5d9ae213381 · inbound

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

Loss-to-Loss Prediction: Scaling Laws for All Datasets How Feature Learning Can Improve Neural Scaling Laws

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T17:07:23.162798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0677a36-301e-4be4-a930-f0e5157640d7 · 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 How Feature Learning Can Improve Neural Scaling Laws

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T11:54:36.822209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c9bdd53-9dd8-435e-8a1a-1b8c506a928c · inbound

Adaptive kernel predictors from feature-learning infinite limits of neural networks cites this paper.

Adaptive kernel predictors from feature-learning infinite limits of neural networks How Feature Learning Can Improve Neural Scaling Laws

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T11:19:06.852656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cd813e0-705a-4b5f-b2d1-10037104500d · inbound

Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models cites this paper.

Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models How Feature Learning Can Improve Neural Scaling Laws

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:29.267386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14832c06-88b9-461a-8b12-2d859f17a77f · 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 How Feature Learning Can Improve Neural Scaling Laws

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:48:49.856090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8d0242f-3bae-4a32-992b-7f413e25c6c9 · 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 How Feature Learning Can Improve Neural Scaling Laws

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:17:37.808219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.