Paper Citation Record · LEDGER

GMAT: Global Memory Augmentation for Transformers

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2006.03274.

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

pith.paper-citation-record.v1
2006.03274 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 2026-08-04T06:34:03.388597+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T13:03:57.828598Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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 94147e3c-9f9d-4755-8644-69fab4e252b8 · inbound

Longformer: The Long-Document Transformer cites this paper.

Longformer: The Long-Document Transformer GMAT: Global Memory Augmentation for Transformers

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:29:58.792930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4baf98c5-7da8-4fa5-b593-c5f0949f73b1 · inbound

Simplified State Space Layers for Sequence Modeling cites this paper.

Simplified State Space Layers for Sequence Modeling GMAT: Global Memory Augmentation for Transformers

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:16:11.473625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cd8af9fd-beb9-4145-93a6-6db61e23e93a · inbound

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory cites this paper.

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory GMAT: Global Memory Augmentation for Transformers

Reference 277

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:03:58.119323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 57751407-4cf9-44f8-80a4-c1e854fe1993 · inbound

MoBA: Mixture of Block Attention for Long-Context LLMs cites this paper.

MoBA: Mixture of Block Attention for Long-Context LLMs GMAT: Global Memory Augmentation for Transformers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:15:46.230102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.