Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Feature Learning Can Improve Neural Scaling Laws
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2409.17858.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T11:54:36.822209Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T18:16:30.548575Z
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 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 2026-08-09T06:31:02.800959+00:00.