Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Transformers Get Rich: Approximation and Dynamics Analysis
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2410.11474.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:37:03.512234Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T13:29:51.021694Z
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 e86347a4-52b2-4b0b-b888-38fbdd3e9914 · inbound
Provable Low-Frequency Bias of In-Context Learning of Representations cites this paper.
Provable Low-Frequency Bias of In-Context Learning of Representations How Transformers Get Rich: Approximation and Dynamics Analysis
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:37:03.512234Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e600c54-8f07-40bd-bf6f-c1db13a9dbd1 · inbound
How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability cites this paper.
How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability How Transformers Get Rich: Approximation and Dynamics Analysis
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T11:20:52.700106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e79b6829-fe16-4969-ac22-6b4b5b7503b2 · inbound
Why Muon Outperforms Adam: A Curvature Perspective cites this paper.
Why Muon Outperforms Adam: A Curvature Perspective How Transformers Get Rich: Approximation and Dynamics Analysis
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T07:06:45.020573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4fbb5b7c-2d6b-4f83-951b-35a53505ab36 · inbound
Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence cites this paper.
Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence How Transformers Get Rich: Approximation and Dynamics Analysis
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T13:18:13.083937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a870090c-9c25-4094-bcbe-e10a9c8160ce · inbound
Learning Dynamics of Chain-of-Thought State Tracking in a Solvable Transformer Model cites this paper.
Learning Dynamics of Chain-of-Thought State Tracking in a Solvable Transformer Model How Transformers Get Rich: Approximation and Dynamics Analysis
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T23:39:05.186239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 794a5b5d-e100-4117-adb7-c6ac245eca7b · inbound
Structure Before Collapse: Transient semantic geometry in next-token prediction cites this paper.
Structure Before Collapse: Transient semantic geometry in next-token prediction How Transformers Get Rich: Approximation and Dynamics Analysis
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T13:29:51.023182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 021d6d9d-87b4-4e06-9406-f26597643ef0 · inbound
Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D cites this paper.
Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D How Transformers Get Rich: Approximation and Dynamics Analysis
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T21:30:59.304489Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.