Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2106.01506.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:01:41.260138Z
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 f7a6cbce-8fe0-4906-bcaf-98470e4b07f1 · inbound
An Attention-based Framework for Fair Contrastive Learning cites this paper.
An Attention-based Framework for Fair Contrastive Learning Transformers are Deep Infinite-Dimensional Non-Mercer Binary Kernel Machines
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:01:41.260138Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-13T06:32:02.005865+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.