Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.16563.
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 2026-08-09T05:16:19.939842Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T04:45:56.947659Z
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 8edd86e1-bb9b-4c92-b7ad-66d400f53683 · inbound
Is In-Context Universality Enough? MLPs are Also Universal In-Context cites this paper.
Is In-Context Universality Enough? MLPs are Also Universal In-Context Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T05:16:19.939842Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7c873cfe-aec8-48eb-9b9e-cbf4a8df8148 · inbound
Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data cites this paper.
Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
Reference 71
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:12:59.161173Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d37e30a-784a-4bf3-ad10-abc312806eec · inbound
Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity cites this paper.
Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
Reference 59
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:45:56.954407Z
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.