Paper Citation Record · LEDGER

Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2106.01506.

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

pith.paper-citation-record.v1
2106.01506 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:26:34.623795Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T00:40:51.780114Z

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 36156b53-9ce4-46d6-aa92-72b10a0db3c8 · inbound

Tokenizing Electron Cloud in Protein-Ligand Interaction Learning cites this paper.

Tokenizing Electron Cloud in Protein-Ligand Interaction Learning Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:34.623795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d21c27a7-3132-4124-b8db-16b98ffab94d · inbound

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods cites this paper.

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:40:51.783188Z

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 ca6aee46-3f77-40e0-820e-38ee175e91af · inbound

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations cites this paper.

On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T20:29:43.854933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91af7fca-44f6-4d9c-ac3b-fbef6509769c · inbound

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers cites this paper.

From Self-Attention to Connection Laplacian: A Unified Operator View of Transformers Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T10:03:27.747512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.