Paper Citation Record · LEDGER

DiJiang: Efficient Large Language Models through Compact Kernelization

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.19928.

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

pith.paper-citation-record.v1
2403.19928 v2

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 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:58:28.311160Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:44:28.059566Z

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 3a76408c-11e9-4d06-9277-56760d66595e · inbound

ReGLA: Refining Gated Linear Attention cites this paper.

ReGLA: Refining Gated Linear Attention DiJiang: Efficient Large Language Models through Compact Kernelization

Reference 7439

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:28.311160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4920c157-511e-4a80-ad76-b027ccf4f504 · inbound

Degrees of Freedom for Linear Attention: Distilling Softmax Attention with Optimal Feature Efficiency cites this paper.

Degrees of Freedom for Linear Attention: Distilling Softmax Attention with Optimal Feature Efficiency DiJiang: Efficient Large Language Models through Compact Kernelization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T20:20:44.462608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6855675e-b011-4ab3-a5a9-5e73e5449f7b · inbound

Functional Attention: From Pairwise Affinities to Functional Correspondences cites this paper.

Functional Attention: From Pairwise Affinities to Functional Correspondences DiJiang: Efficient Large Language Models through Compact Kernelization

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:02:50.317597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 792c7e69-cce9-44df-891a-04d3f9b4752b · inbound

Morphing into Hybrid Attention Models cites this paper.

Morphing into Hybrid Attention Models DiJiang: Efficient Large Language Models through Compact Kernelization

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:44:28.061053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.