Paper Citation Record · LEDGER

Are Transformers universal approximators of sequence-to-sequence functions?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:1912.10077.

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

pith.paper-citation-record.v1
1912.10077 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:13:25.754027Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:44.309933Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 70a371b3-fb9d-458f-818c-9b2bd6025f81 · inbound

Transformers versus the EM Algorithm in Multi-class Clustering cites this paper.

Transformers versus the EM Algorithm in Multi-class Clustering Are Transformers universal approximators of sequence-to-sequence functions?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T17:13:25.754027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db5b7be2-258f-440b-a1a8-592ca5d26ecb · inbound

Solving Empirical Bayes via Transformers cites this paper.

Solving Empirical Bayes via Transformers Are Transformers universal approximators of sequence-to-sequence functions?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T20:21:58.396306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deadf153-e3be-404f-ad12-0701ed62cc0b · inbound

Rethinking Causal Mask Attention for Vision-Language Inference cites this paper.

Rethinking Causal Mask Attention for Vision-Language Inference Are Transformers universal approximators of sequence-to-sequence functions?

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:33.425489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 908a9664-6277-4c40-80e4-df1aa557d70f · inbound

GSA-TTS : Toward Zero-Shot Speech Synthesis based on Gradual Style Adaptor cites this paper.

GSA-TTS : Toward Zero-Shot Speech Synthesis based on Gradual Style Adaptor Are Transformers universal approximators of sequence-to-sequence functions?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:07.766917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8a408ed-f361-4636-a630-e614ce3f8825 · inbound

Transformers Are Universally Consistent cites this paper.

Transformers Are Universally Consistent Are Transformers universal approximators of sequence-to-sequence functions?

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:06.521514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2084ee9-b7bd-45a0-8515-4d9860c7f7c7 · inbound

Existing Large Language Model Unlearning Evaluations Are Inconclusive cites this paper.

Existing Large Language Model Unlearning Evaluations Are Inconclusive Are Transformers universal approximators of sequence-to-sequence functions?

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T12:05:56.351628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e11c36a9-ba77-42ee-894a-2d3458285132 · inbound

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization cites this paper.

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization Are Transformers universal approximators of sequence-to-sequence functions?

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T06:10:21.358940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84a07a37-1edc-468f-b252-8735743043ca · inbound

Theoretical Analysis of Positional Encodings in Transformer Models: Impact on Expressiveness and Generalization cites this paper.

Theoretical Analysis of Positional Encodings in Transformer Models: Impact on Expressiveness and Generalization Are Transformers universal approximators of sequence-to-sequence functions?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:31.995796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 036772fa-2944-4984-b8d5-206c295f6300 · inbound

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques cites this paper.

Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques Are Transformers universal approximators of sequence-to-sequence functions?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:35.295805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e24742c2-24f2-4c94-9fb9-1d5775b6f79a · inbound

Time Resolution Independent Operator Learning cites this paper.

Time Resolution Independent Operator Learning Are Transformers universal approximators of sequence-to-sequence functions?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T20:34:09.205665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36941140-0c54-4d2b-a716-20ac10fdcd4c · inbound

On the Mathematical Impossibility of Safe Universal Approximators cites this paper.

On the Mathematical Impossibility of Safe Universal Approximators Are Transformers universal approximators of sequence-to-sequence functions?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T20:41:55.587713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53fa0876-b32a-4d23-8dda-130b4925efb4 · inbound

Decoding Consumer Preferences Using Attention-Based Language Models cites this paper.

Decoding Consumer Preferences Using Attention-Based Language Models Are Transformers universal approximators of sequence-to-sequence functions?

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.078612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50e79fa4-7a8d-4670-8b8a-57c90e4b4820 · inbound

Context-aware Rotary Position Embedding cites this paper.

Context-aware Rotary Position Embedding Are Transformers universal approximators of sequence-to-sequence functions?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T11:09:08.432049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f78fd59-44ff-486a-a621-39ee8527af08 · inbound

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling cites this paper.

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling Are Transformers universal approximators of sequence-to-sequence functions?

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:51:50.868831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fcd3909e-358c-4cc6-a20f-584c92128715 · inbound

Unraveling Syntax: Language Modeling and the Substructure of Grammars cites this paper.

Unraveling Syntax: Language Modeling and the Substructure of Grammars Are Transformers universal approximators of sequence-to-sequence functions?

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T12:47:21.283300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f10c16c-47ba-4cd8-a5e9-c11b220203b2 · inbound

Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Self-Attention Transformers cites this paper.

Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Self-Attention Transformers Are Transformers universal approximators of sequence-to-sequence functions?

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:40:50.496470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d6286ac3-109e-4068-89e0-8b0de8876a56 · inbound

Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees cites this paper.

Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees Are Transformers universal approximators of sequence-to-sequence functions?

Reference 174

Resolution: unresolved; no resolver link, observed 2026-08-03T03:21:49.824957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f01a6dbb-b2cd-48d4-9662-d8b2c6245f2a · inbound

Gating Enables Curvature: A Geometric Expressivity Gap in Attention cites this paper.

Gating Enables Curvature: A Geometric Expressivity Gap in Attention Are Transformers universal approximators of sequence-to-sequence functions?

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:20:11.376181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2ecaae8f-ef3e-4e72-b0e9-bd0d77e79bfc · inbound

Continuous transformations of probability measures and their transport representations cites this paper.

Continuous transformations of probability measures and their transport representations Are Transformers universal approximators of sequence-to-sequence functions?

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:56:47.232493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 79ee8e96-593f-4e46-a052-e5a04f30d8ae · inbound

Progressive Approximation in Deep Residual Networks: Theory and Validation cites this paper.

Progressive Approximation in Deep Residual Networks: Theory and Validation Are Transformers universal approximators of sequence-to-sequence functions?

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:41:16.148117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec91d9a6-9208-4372-b079-1b6ddd27492c · inbound

How Does Attention Help? Insights from Random Matrices on Signal Recovery from Sequence Models cites this paper.

How Does Attention Help? Insights from Random Matrices on Signal Recovery from Sequence Models Are Transformers universal approximators of sequence-to-sequence functions?

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:05:58.700918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c2348a66-465a-4e22-8a00-5423acb01eb5 · inbound

A generative pre-trained transformer with Kerr-soliton attention cites this paper.

A generative pre-trained transformer with Kerr-soliton attention Are Transformers universal approximators of sequence-to-sequence functions?

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:04:46.486774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31e7d9a7-ca2e-478e-a3d4-09830604fd58 · inbound

The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail cites this paper.

The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail Are Transformers universal approximators of sequence-to-sequence functions?

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-28T19:12:34.913911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d4e3d0f4-2e32-4f57-a427-31644db06327 · inbound

Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair cites this paper.

Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repair Are Transformers universal approximators of sequence-to-sequence functions?

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:46:48.999772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f16d0fac-d2c0-41da-9f12-fc68533b825a · 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 universal approximators of sequence-to-sequence functions?

Reference 110

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:44.311684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 21bb62ce-7cc0-4e08-8803-e89fb4644a7a · inbound

Pre-Strings Lectures on Artificial Intelligence cites this paper.

Pre-Strings Lectures on Artificial Intelligence Are Transformers universal approximators of sequence-to-sequence functions?

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T06:14:03.658427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f95da0d6-cff4-4aa6-8be6-00989bc4ecdb · inbound

On Transformer Dynamics cites this paper.

On Transformer Dynamics Are Transformers universal approximators of sequence-to-sequence functions?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T05:49:28.691589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 847e8761-c387-4425-be05-f1e3f3272ba1 · inbound

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex Are Transformers universal approximators of sequence-to-sequence functions?

Reference 213

Resolution: unresolved; no resolver link, observed 2026-07-31T23:52:08.233130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4257aa79-2604-4872-89a4-6e4b6ef5cb9a · inbound

Identifiability-Aware Source Apportionment in City-Scale Advection-Diffusion Systems cites this paper.

Identifiability-Aware Source Apportionment in City-Scale Advection-Diffusion Systems Are Transformers universal approximators of sequence-to-sequence functions?

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-04T01:37:40.900908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.