Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The boundary of neural network trainability is fractal
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.06184.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:39:38.259249Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T00:43:10.358066Z
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 0de319b2-f0c5-4213-9eef-8af26bb01d74 · inbound
Mapping the Edge of Chaos: Fractal-Like Boundaries in The Trainability of Decoder-Only Transformer Models cites this paper.
Mapping the Edge of Chaos: Fractal-Like Boundaries in The Trainability of Decoder-Only Transformer Models The boundary of neural network trainability is fractal
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:39:38.259249Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac025386-8e78-47e3-b420-663ccce4141c · inbound
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions cites this paper.
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions The boundary of neural network trainability is fractal
Reference 61
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T00:43:10.423646Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 93310c06-d348-4dca-b722-4524c5a892f4 · inbound
Implicit Bias of SGD in Multivariate ReLU Networks: Effective Width Collapse cites this paper.
Implicit Bias of SGD in Multivariate ReLU Networks: Effective Width Collapse The boundary of neural network trainability is fractal
Reference 120
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T01:11:50.444218Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2dd9f7be-b165-4543-805f-f9c7ca376a7b · inbound
Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification cites this paper.
Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification The boundary of neural network trainability is fractal
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T07:42:46.557974Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d208ca87-b1eb-4a30-a6fa-78f5ac66a57c · inbound
Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification cites this paper.
Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification The boundary of neural network trainability is fractal
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T03:23:47.495003Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.