Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
GradMax: Growing Neural Networks using Gradient Information
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2201.05125.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T01:13:15.449012Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-09T04:35:57.532084Z
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 18ab6f87-14c5-48b3-94e2-cd7596422772 · inbound
Growing Neural Networks: Dynamic Evolution through Gradient Descent cites this paper.
Growing Neural Networks: Dynamic Evolution through Gradient Descent GradMax: Growing Neural Networks using Gradient Information
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T01:13:15.449012Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 52e060a5-2a58-4eef-be44-9a53f79c9a4e · inbound
Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence cites this paper.
Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence GradMax: Growing Neural Networks using Gradient Information
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:43:59.233421Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5af9cb84-4daa-4cc5-9e3b-aa3b19fc1164 · inbound
Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts cites this paper.
Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts GradMax: Growing Neural Networks using Gradient Information
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T20:40:36.476897Z
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 c28fc6e0-50ac-4c69-a32e-2076e3f9913b · inbound
SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning cites this paper.
SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning GradMax: Growing Neural Networks using Gradient Information
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T05:27:09.807211Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b250d378-4c49-4d5f-abcf-2d975b75b51a · inbound
An optimal control approach for neural network architecture adaptation with a posteriori error estimation cites this paper.
An optimal control approach for neural network architecture adaptation with a posteriori error estimation GradMax: Growing Neural Networks using Gradient Information
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T04:35:57.533655Z
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.