Paper Citation Record · LEDGER

ReGLA: Refining Gated Linear Attention

As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2502.01578.

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

pith.paper-citation-record.v1
2502.01578 v3

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:58:28.384982Z

measured 13 of 13 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T15:27:55.566795Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:41:11.033629Z

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy3
unresolved8
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 55091fc3-93a7-4ee2-9984-e9de5b8d0c2b · outbound

This paper cites AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning.

ReGLA: Refining Gated Linear Attention AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:28.372580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67b72f18-a996-4fd6-a9c5-4db836ee0a2a · outbound

This paper cites An Attention Free Transformer.

ReGLA: Refining Gated Linear Attention An Attention Free Transformer

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-09T14:58:28.384982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e09df79-3139-4610-9e76-7eed6b46fbfa · outbound

This paper cites Stephanie Lin, Jacob Hilton, and Owain Evans.

ReGLA: Refining Gated Linear Attention Stephanie Lin, Jacob Hilton, and Owain Evans

Reference 626

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:58:28.623021Z

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 329f43b7-fb04-4b91-b630-0dc4d7226530 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ReGLA: Refining Gated Linear Attention Distilling the Knowledge in a Neural Network

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:28.355406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5764fd25-4ea5-4e01-be73-b21e5a4f12cc · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

ReGLA: Refining Gated Linear Attention LLaMA: Open and Efficient Foundation Language Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:28.381778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 564530a2-a8de-4200-9114-808576bd5dc0 · outbound

This paper cites In 8th International Conference on Learning Representa- tions, ICLR 2020, Addis Ababa, Ethiopia, April 26- 30, 2020.

ReGLA: Refining Gated Linear Attention In 8th International Conference on Learning Representa- tions, ICLR 2020, Addis Ababa, Ethiopia, April 26- 30, 2020

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:58:28.673120Z

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 db6bed96-5954-459f-9948-1ee58b01c424 · outbound

This paper cites an unresolved cited work.

ReGLA: Refining Gated Linear Attention Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-09T14:58:28.493332Z

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 75963c56-8c83-4724-bfc4-7bb129be203f · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

ReGLA: Refining Gated Linear Attention Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:28.345359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1e10dd2-ba04-4478-a0b4-d53b3273d48b · outbound

This paper cites Linearizing Large Language Models.

ReGLA: Refining Gated Linear Attention Linearizing Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:28.369659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59a8ee0b-03fe-4056-ba79-f9019577f060 · outbound

This paper cites Huanru Henry Mao.

ReGLA: Refining Gated Linear Attention Huanru Henry Mao

Reference 6565

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:58:28.564114Z

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

This paper cites DiJiang: Efficient Large Language Models through Compact Kernelization.

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 42c5fc2d-3f8b-4b6a-9074-daef2b08a0dd · outbound

This paper cites Retentive Network: A Successor to Transformer for Large Language Models.

ReGLA: Refining Gated Linear Attention Retentive Network: A Successor to Transformer for Large Language Models

Reference 9366

Resolution: unresolved; no resolver link, observed 2026-08-09T14:58:28.378606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e588fc1d-a70d-4b1a-a4d4-d26bcdc64ce0 · inbound

MDN: Parallelizing Stepwise Momentum for Delta Linear Attention cites this paper.

MDN: Parallelizing Stepwise Momentum for Delta Linear Attention ReGLA: Refining Gated Linear Attention

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:41:11.049805Z

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.