Paper Citation Record · LEDGER

Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2307.14023.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2307.14023 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:49:39.606729Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T10:29:44.325402Z

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 dcb8e094-1582-46e3-a39a-aa287aa80415 · inbound

Understanding Factual Recall in Transformers via Associative Memories cites this paper.

Understanding Factual Recall in Transformers via Associative Memories Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T19:41:31.784776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a54420d-e062-4547-a14d-f7cd99503ed5 · inbound

Attention Mechanism, Max-Affine Partition, and Universal Approximation cites this paper.

Attention Mechanism, Max-Affine Partition, and Universal Approximation Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:49:39.606729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 826301cb-e573-4121-bba2-e1db4ad29938 · inbound

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions cites this paper.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.122910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9522d1fd-1b49-4d0b-860c-5c4098e0af94 · inbound

Transformer Approximations from ReLUs cites this paper.

Transformer Approximations from ReLUs Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:51:30.221196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 59b473ed-536e-41a5-bab9-36ed51907084 · inbound

Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime cites this paper.

Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

Reference 111

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:44.327238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9f7f622e-23d4-439d-815b-2f3e9dd54550 · inbound

Attention-based representations for multi-task computation cites this paper.

Attention-based representations for multi-task computation Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T00:18:21.293789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.