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Paper Citation Record · LEDGER

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

As of 18 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 100 inbound Pith citation observations for arXiv:2402.19427.

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

pith.paper-citation-record.v1
2402.19427 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T06:58:17.370396Z

measured 140 of 140 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 100 of 109 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:19:30.286857Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

40 of 40 outbound references displayed

  • verified exact34
  • verified fuzzy4
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

8
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 87cf6716-212c-4ad6-9893-b3cc7fb964fc · outbound

This paper cites GPT-4 Technical Report.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models GPT-4 Technical Report

Reference 1

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local_arxiv, observed 2026-05-15T06:58:17.404105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 09c0fc6e-96a3-46af-bd4f-7379ef746ff6 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Neural Machine Translation by Jointly Learning to Align and Translate

Reference 2

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local_arxiv, observed 2026-05-15T06:58:17.411532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 3159a262-5e7d-4778-98d0-e2f5f701be8f · outbound

This paper cites Longformer: The Long-Document Transformer.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Longformer: The Long-Document Transformer

Reference 3

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local_arxiv, observed 2026-05-15T06:58:17.418193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1eec6377-7683-443d-900a-c9ea21c084a4 · outbound

This paper cites Quasi-Recurrent Neural Networks.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Quasi-Recurrent Neural Networks

Reference 4

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local_arxiv, observed 2026-05-15T06:58:17.423878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:abb9432f9651d1a6a935946395fa4c66e3a2fd727a88c49432d513470eb5270d

Observation 2318a795-c3f2-4416-a960-ba6f7c47c756 · outbound

This paper cites T.Brown,B.Mann,N.Ryder,M.Subbiah,J.D.Kaplan,P.Dhariwal,A.Neelakantan,P.Shyam,G.Sastry, A.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models T.Brown,B.Mann,N.Ryder,M.Subbiah,J.D.Kaplan,P.Dhariwal,A.Neelakantan,P.Shyam,G.Sastry, A

Reference 5

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raw_fallback, observed 2026-05-15T06:58:17.614165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:f3b5c7d9e1f6e2635b600e9735e2dad07484f64d0a4a27ebb4cf7bcc6f595ac3

Observation c33bcbbb-ef9b-4a3e-9f87-9d6a57a05915 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Generating Long Sequences with Sparse Transformers

Reference 6

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local_arxiv, observed 2026-05-15T06:58:17.469338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:f43e2edfeb382af06f9e2f3266b09e9c72024555ceafe2962bfcd89478ab5378

Observation 55ee1edb-eb88-4c0f-8c5f-98566dd9df0f · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 7

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local_arxiv, observed 2026-05-15T06:58:17.474817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5b5c40d6-e234-4fdd-b727-9b7e275ac8ae · outbound

This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 8

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arxiv_id, observed 2026-05-15T06:58:17.482210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:824cbb2279efe326fe536e6522b9896d7e6872c58b0fb17e75d09a2a9ba6e33c

Observation 6e4486d7-ce5c-4c22-a8db-ec209bdd790c · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 9

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local_arxiv, observed 2026-05-15T06:58:17.488268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e19687ef-b041-4cc8-a9c0-99459888b257 · outbound

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

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 10

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local_arxiv, observed 2026-05-15T06:58:17.494676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 4ff28c73-b19e-4a42-946a-cbcbaebd3c2c · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Efficiently Modeling Long Sequences with Structured State Spaces

Reference 11

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local_arxiv, observed 2026-05-15T06:58:17.501302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 0d3ed0ee-309d-4b95-afe4-dfd78e21c628 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Gaussian Error Linear Units (GELUs)

Reference 12

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local_arxiv, observed 2026-05-15T06:58:17.507685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 88e56c38-da17-4c57-9681-d19afd23902e · outbound

This paper cites Training Compute-Optimal Large Language Models.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Training Compute-Optimal Large Language Models

Reference 13

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local_arxiv, observed 2026-05-15T06:58:17.513427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 4785e0e2-ffce-4e15-87fe-1fa1c43050d1 · outbound

This paper cites Repeat After Me: Transformers are Better than State Space Models at Copying.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 14

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arxiv_id, observed 2026-05-15T06:58:17.519265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation df4db80d-37ac-4ded-a8b4-632fe1c177d5 · outbound

This paper cites Mistral 7B.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Mistral 7B

Reference 15

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local_arxiv, observed 2026-05-15T06:58:17.525125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 4d88dc55-0d64-41e8-97b3-4810ca6b7ef9 · outbound

This paper cites an unresolved cited work.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Unresolved cited work

Reference 16

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raw_fallback, observed 2026-05-15T06:58:17.617708Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 79320dd5-00a0-45de-9a30-1e04cd95b9ee · outbound

This paper cites Scaling Laws for Neural Language Models.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Scaling Laws for Neural Language Models

Reference 17

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local_arxiv, observed 2026-05-15T06:58:17.532642Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 2e74e5b8-18d7-4c59-85a4-db39883f2d70 · outbound

This paper cites GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling

Reference 18

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arxiv_id, observed 2026-05-15T06:58:17.539189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation d642502c-c943-4d42-a12d-22b4c622f805 · outbound

This paper cites Advances in Neural Information Processing Systems,36.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Advances in Neural Information Processing Systems,36

Reference 19

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raw_fallback, observed 2026-05-15T06:58:17.629689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:bbd2b3b7f15b68e60ffca227b6bba6e24b6b2d022de0486fe7f003c5fe147116

Observation 92df91aa-32dc-4d4b-ab6a-87b2f27590c3 · outbound

This paper cites Decoupled Weight Decay Regularization.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Decoupled Weight Decay Regularization

Reference 20

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local_arxiv, observed 2026-05-15T06:58:17.544914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:6ee9cdad3350979902c4d235e64cac69cb8e4af2c9ddb5e00c21271cf13e20ff

Observation 5faca210-37cd-46fe-b80f-67a2fe77813a · outbound

This paper cites Parallelizing Linear Recurrent Neural Nets Over Sequence Length.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Parallelizing Linear Recurrent Neural Nets Over Sequence Length

Reference 21

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local_arxiv, observed 2026-05-15T06:58:17.550241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 6a8aa7f5-c97c-4e5a-bd25-04adefc0fc7c · outbound

This paper cites Long Range Language Modeling via Gated State Spaces.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Long Range Language Modeling via Gated State Spaces

Reference 22

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arxiv_id, observed 2026-05-15T06:58:17.555498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation f6b4b095-2935-4e4d-bbdd-fd1421e6287c · outbound

This paper cites Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues

Reference 23

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arxiv_id, observed 2026-05-15T06:58:17.560747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:d4aabd3ee52258038f93df524d579721dbed709d8a6f3e9088116f4c64b0fa6d

Observation 18c879ba-ebab-48c9-b250-077258870c42 · outbound

This paper cites Hyena Hierarchy: Towards Larger Convolutional Language Models.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Hyena Hierarchy: Towards Larger Convolutional Language Models

Reference 24

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arxiv_id, observed 2026-05-15T06:58:17.566956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 992899c5-c7d4-46d2-8001-8d4a8435d8cd · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 25

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local_arxiv, observed 2026-05-15T06:58:17.573112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:8527de405acf03e8242078e95ee91967a4b5660768cc4f13cdeffafd38047a40

Observation 8850c0a0-df23-4695-b680-4f56978a3535 · outbound

This paper cites Fast Transformer Decoding: One Write-Head is All You Need.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Fast Transformer Decoding: One Write-Head is All You Need

Reference 26

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local_arxiv, observed 2026-05-15T06:58:17.578807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:74693630ca4d2633598fc2b779c7a317aeada65690f9f49a048780044214e721

Observation 790bdada-5db6-472b-a1e9-83e7d0303380 · outbound

This paper cites GLU Variants Improve Transformer.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models GLU Variants Improve Transformer

Reference 27

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local_arxiv, observed 2026-05-15T06:58:17.583949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:28028d7de4887e322a049774dcac7a087ff47f6805102769f1ba55f619c15a90

Observation 3ff9f499-89a9-46ad-9ed4-a3bdf03cfa41 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 28

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local_arxiv, observed 2026-05-15T06:58:17.592017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:2254b8ac8824be503e4d48ecc67e66a4812b2aeb62955ac4c33e55ad40cde384

Observation 73b1b397-82a6-4ed8-9f81-2f94adb8ff41 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Simplified State Space Layers for Sequence Modeling

Reference 29

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arxiv_id, observed 2026-05-16T08:16:11.899530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:7c1ae71fda161e76c3e8508d17146a45be8150da14e63d5bbd4f7777eabe47f5

Observation 275ae357-f483-4b42-a1b2-b4e2a2ec6245 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 30

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local_arxiv, observed 2026-05-15T06:58:17.603924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:acf521599c5730f8580a9c0587c0a9f056391229bbb65d8f2b26c9cbb0311054

Observation f3a76fb5-52a8-4a4c-9ab1-75af503ea322 · outbound

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

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Retentive Network: A Successor to Transformer for Large Language Models

Reference 31

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local_arxiv, observed 2026-05-15T06:58:17.609791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:edcf2aa592780a45801836a02c66dfc7ba0699b33868ae0b1f58053cfc34d8d6

Observation 9c1fdd02-67de-4fbd-b50f-4c9d2e9dc6a2 · outbound

This paper cites Long Range Arena: A Benchmark for Efficient Transformers.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Long Range Arena: A Benchmark for Efficient Transformers

Reference 32

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arxiv_id, observed 2026-05-15T06:58:17.429964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:6abb671202e51f84ca6aa0cee2c723df57b52c919321f718349d95da526c6994

Observation baa54c77-d192-4123-9937-305d4535e876 · outbound

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

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 33

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local_arxiv, observed 2026-05-15T06:58:17.435569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:8eb4e7b8e781560237e6a128774eadc2091c342dfba1e1ab03ab6d49868b1e9f

Observation eedd0109-dc5b-4176-964b-d1704c146855 · outbound

This paper cites MambaByte: Token-free Selective State Space Model.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models MambaByte: Token-free Selective State Space Model

Reference 34

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:19ae4ea8b9b53ee8cb7f1abe404a8124b4a96e6818622d3f3f26b3fba7accc01

Observation 61b73720-64f8-4586-9849-e616c6cf35f9 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 35

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local_arxiv, observed 2026-05-15T06:58:17.450878Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:5b83d8b5d2b33053ae4ca36977747f66ced2369fe7b82eb5ca438cfa2f566e61

Observation 9073705e-3dfd-4def-a342-4df21a38486d · outbound

This paper cites An Attention Free Transformer.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models An Attention Free Transformer

Reference 36

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arxiv_id, observed 2026-05-15T06:58:17.456663Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:14a916a909a51ad031283d720c1104adc980e9955526bf811f68cf416fc403bd

Observation 2c6f7f69-0934-4197-bfbc-f9f3fd7320ec · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 37

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local_arxiv, observed 2026-05-15T06:58:17.463618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:20da01a2a5d759780576c9573e478400db0497ff7f85e1591b9755729b859ced

Observation 826ca3f4-8586-499a-ab66-85fae5056b78 · outbound

This paper cites (13) We mark all complex variables with˜·for clarity.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models (13) We mark all complex variables with˜·for clarity

Reference 38

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raw_fallback, observed 2026-05-15T06:58:17.634022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:7312feec9ae53c3bf43ecac46cd4646e302a123882561b21588251667b2edb1d

Observation 69dcc9dd-59ff-4712-a0e5-dec63b133fad · outbound

This paper cites an unresolved cited work.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models Unresolved cited work

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:b4fb0c6632329682c735cb67765f9f9041fa46dc2cf9d11fc1d0365f27a1a213

Observation 9cf9927a-fe44-4c45-9151-5c9b99f03a47 · outbound

This paper cites On the left, we compare the performance of different models trained with sequence length 2048, evaluated with a sequence length of up to 32,768.

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models On the left, we compare the performance of different models trained with sequence length 2048, evaluated with a sequence length of up to 32,768

Reference 40

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raw_fallback, observed 2026-05-15T06:58:17.625874Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:58:17.370396Z digest=sha256:d913d83da1a710a006323ca5c965f05e0d45c3820d7c38bea924c7bcb4aa0b5f

Pith citing papers

Observation 06955ea7-2766-403c-925b-c1127bbe829c · inbound

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality cites this paper.

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 29

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arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-11T12:16:25.390683Z digest=sha256:c10ddc3be4d5575a32108640efccd16eaf618b59ebfb2efbadc1a07c86653922

Observation 2ea19612-848c-4781-bfc9-b911c4afd93d · inbound

An Empirical Study of Mamba-based Language Models cites this paper.

An Empirical Study of Mamba-based Language Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 13

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local_arxiv, observed 2026-05-18T10:31:03.952121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T10:31:03.777169Z digest=sha256:e47cee85d7d570ab7b2cabefb1529765e43adfbc1bf6035fe903c115840450e5

Observation c3d1962c-1f0b-4826-be5d-5803d408ee51 · inbound

Learning to (Learn at Test Time): RNNs with Expressive Hidden States cites this paper.

Learning to (Learn at Test Time): RNNs with Expressive Hidden States Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 18

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arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T05:20:12.134340Z digest=sha256:7f4e4f6be8f66f70d214e9391649c8dbe69d35adf87a6998245226cbb9e4501c

Observation 33723dc7-91db-4d02-a714-deb19bd93a21 · inbound

LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation cites this paper.

LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 10

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metadata mismatch
local_arxiv, observed 2026-05-23T18:33:19.409017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-23T18:31:35.391674Z digest=sha256:e48b8b6afdbc8656c75f642d7246be9ce1c8ec4afb19b1ad54c2d19edeca727a

Observation 00c89e75-9697-4269-b1d7-89f40bde8b1c · inbound

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map cites this paper.

MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:28:28.652035Z digest=sha256:661d03bfaa1aebf15d5a4d106f07c3e1f957709217866603de5da60a215c6d5a

Observation 4ccf5561-4705-4e92-ba3c-7ceb6021ba38 · inbound

Selective Attention: Enhancing Transformer through Principled Context Control cites this paper.

Selective Attention: Enhancing Transformer through Principled Context Control Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:12:32.680612Z digest=sha256:0f22c3919e01f232b7b801049552aa9cf3d83aec4674ac77bd39bdd4378db5ea

Observation 221fdb52-126f-4b54-bf02-399f2e468f51 · inbound

Hymba: A Hybrid-head Architecture for Small Language Models cites this paper.

Hymba: A Hybrid-head Architecture for Small Language Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 85

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:20:32.762403Z digest=sha256:bb7665a5f2f6e68791d71c2510954715563dce0733a7b9bc9f0cdb63e6bbe9aa

Observation 57bfdbea-7147-42fb-af31-eec34fd06814 · inbound

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning cites this paper.

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2018

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T12:13:55.170833Z digest=sha256:43554e097eca386a26acf9ac696472a9902930d2d01daeb1ca0225e5e48fd5e2

Observation c080b9ca-69ca-40fd-978a-c69ae7276dd0 · inbound

Attamba: Attending To Multi-Token States cites this paper.

Attamba: Attending To Multi-Token States Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 6

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T11:56:33.047492Z digest=sha256:d2c2454570c0ba147c999e1558dc5e9acdd12da5889488a42e9645c859596c5c

Observation 45fc5245-8876-4b22-b5af-21bb4c1cdacf · inbound

Marconi: Prefix Caching for the Era of Hybrid LLMs cites this paper.

Marconi: Prefix Caching for the Era of Hybrid LLMs Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2022

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T10:19:00.101454Z digest=sha256:7aecfa51d77c7f9cd912b4d262a5e8db923a9088098e6a8de87cbf34c1062ce0

Observation a6cb877e-6a1a-4b05-8e12-be1c1d90dc4f · inbound

MAL: Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision Performance cites this paper.

MAL: Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision Performance Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 12

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no resolver link, observed 2026-08-11T15:45:21.875332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:45:21.875332Z digest=sha256:410ee2855cdbe5c8ec440d622cb606ca0d56392db795c938c279ad8679dbf63a

Observation 35461595-eefe-4b9b-b199-58a8ab6265e1 · inbound

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models cites this paper.

Expansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

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no resolver link, observed 2026-08-11T13:17:49.920570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:17:49.920570Z digest=sha256:289da5c0d5ee3f53128832c925abcc20f977f77722d5c2ff44ddc95f1a43848a

Observation 68e731b5-6662-4f69-862a-7e80fd990183 · inbound

On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages cites this paper.

On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

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no resolver link, observed 2026-08-11T00:46:47.564683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:46:47.564683Z digest=sha256:095e9dcbcec8787b5f605f79c0334aacd17c0ebf2ce42e5d141209400ac43163

Observation ff742bf8-2803-48b4-990b-dd784d4ac900 · inbound

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing cites this paper.

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 12

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no resolver link, observed 2026-08-10T22:52:33.407687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:52:33.407687Z digest=sha256:672056631ac64d8fb67faa07afc81160b3c4f323d4706d91be5ab0b40d01bc39

Observation 4ad863bf-577d-4590-9214-3901ded4aafd · inbound

Titans: Learning to Memorize at Test Time cites this paper.

Titans: Learning to Memorize at Test Time Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 29

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arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-14T22:08:14.982302Z digest=sha256:0138b9053fd7586eb7e163fd7c1e9aa41ce119a8d8ad315155be15cc5dfdf5b3

Observation 44ea67d5-8c86-4734-b520-6a1dbc847437 · inbound

MSWA: Refining Local Attention with Multi-ScaleWindow Attention cites this paper.

MSWA: Refining Local Attention with Multi-ScaleWindow Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 18

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no resolver link, observed 2026-08-10T22:39:31.411825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:39:31.411825Z digest=sha256:2f5d2c312826634a0ebb1f87baac9cbc794d32aa31c81c9ff7cc263b73e06475

Observation 7ba4b453-34a9-4e90-a3e4-bb2cb35c86f5 · inbound

Test-time regression: a unifying framework for designing sequence models with associative memory cites this paper.

Test-time regression: a unifying framework for designing sequence models with associative memory Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:22:07.027580Z digest=sha256:275b98c3922a9fe8d93f2c3c94a44f4091992d6263f84e4e0a2360f4e7f8344f

Observation 64bd7a44-1de4-491a-963a-53bee77dd574 · inbound

GRAMA: Adaptive Graph Autoregressive Moving Average Models cites this paper.

GRAMA: Adaptive Graph Autoregressive Moving Average Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T16:57:29.078058Z digest=sha256:460a8f6215de2303f00889aa70a742888649f3035d57f75f61325659e9438eff

Observation e9a88c47-c086-40f5-8f71-0df4b92ab72e · inbound

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing cites this paper.

Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:19:23.945339Z digest=sha256:46048f460192f69bffb6f82e4190725e873d984854f680ea119f005b87b40878

Observation 056e3391-19a9-482c-8f81-e5c0ee415e9c · inbound

On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach cites this paper.

On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

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no resolver link, observed 2026-08-09T13:05:31.591911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:05:31.591911Z digest=sha256:3886652d74f251ed266410d362533e598cccf51f211a025e5a18444ed7ed620d

Observation b16394b4-5d9a-4a44-8eae-af6350b2a513 · inbound

An Uncertainty Principle for Linear Recurrent Neural Networks cites this paper.

An Uncertainty Principle for Linear Recurrent Neural Networks Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2022

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no resolver link, observed 2026-08-07T22:10:15.888345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T22:10:15.888345Z digest=sha256:d14175f903f33ee8892f3addbf282fedf0dd0c074ece8983886855d9bc63cb51

Observation be4a3871-60c1-495e-ab58-550edd005235 · inbound

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention cites this paper.

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 58

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local_arxiv, observed 2026-05-16T23:46:30.073118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-16T23:46:29.975858Z digest=sha256:dd0c18e6e467cecbddd0c52db2262c62222e47a84b29a042615496cec76190b8

Observation 0320d96b-4f2a-41e7-99e8-7e1a7cace126 · inbound

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization cites this paper.

It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T12:19:30.286857Z digest=sha256:dfea3c105e086b6b7434ec068c018b53b6f3461c28d25d951dda21902401d837

Observation e208c05f-fcd9-4ad1-babd-562f36e5412e · inbound

ForgeBench: A Machine Learning Benchmark Suite and Auto-Generation Framework for Next-Generation HLS Tools cites this paper.

ForgeBench: A Machine Learning Benchmark Suite and Auto-Generation Framework for Next-Generation HLS Tools Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 28

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:34:26.698505Z digest=sha256:408ce83ce95457a73bafe5d54a5b516b4bf4ff3ee6e74acd8253f65e152948f3

Observation f2dfb2a3-992d-46be-9a70-efcac9a03df4 · inbound

LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities cites this paper.

LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:16:29.630423Z digest=sha256:2770f77db080311c85616f1ebdea72bd3aa017645f784a3bffcc66a19ddb6c9a

Observation 0fa4d082-08dd-4cc4-90f1-f21da8c1df1d · inbound

Quantifying Memory Utilization with Effective State-Size cites this paper.

Quantifying Memory Utilization with Effective State-Size Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 18

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no resolver link, observed 2026-08-16T05:58:22.403929Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-16T05:58:22.403929Z digest=sha256:d7000775337dea0ac5ca5bc4276b534167891366d548a1280dad6a3ddabc4149

Observation 69fb5a92-dc91-4efb-abf8-271cf9a58e50 · inbound

Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook cites this paper.

Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 50

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:40:07.702962Z digest=sha256:1e7efec0d21e5998863af388e8ffa528c38c22891e005b35d28b4a881bb8f02c

Observation def813ee-7313-44f2-919a-5f6881eec9ed · inbound

Reasoning Capabilities and Invariability of Large Language Models cites this paper.

Reasoning Capabilities and Invariability of Large Language Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:40:57.775150Z digest=sha256:1279423b3ddddc05431c382e4fc241d33bf659850f293629222a83c55ba5cfea

Observation ced32ed0-de43-45dd-84c4-b0d6b4dff9f9 · inbound

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling cites this paper.

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 25

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no resolver link, observed 2026-08-07T14:32:36.596300Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:32:36.596300Z digest=sha256:1db94b818011b84556c2a8a1108d1d75654673b814ce62a60a3037c9cbb568d8

Observation 6e69752c-4362-44d3-abba-04e2c13ff613 · inbound

Revisiting Glorot Initialization for Long-Range Linear Recurrences cites this paper.

Revisiting Glorot Initialization for Long-Range Linear Recurrences Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2014

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no resolver link, observed 2026-08-07T14:14:08.747322Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:14:08.747322Z digest=sha256:0f5052ac9c6d808836430b13ff5b465eddc6f4cc78652caada172043ef354f56

Observation 4e84e033-a2a6-4b5f-af98-82f51edd9fb1 · inbound

Sparsified State-Space Models are Efficient Highway Networks cites this paper.

Sparsified State-Space Models are Efficient Highway Networks Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2018

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source=pdf_text observed=2026-08-07T13:54:45.020020Z digest=sha256:dd37991c1a1d52ed0abd5fc8416ec0cd5230ed7d4bc9a1cec26847af2cebed3b

Observation 62752f89-cdd0-4fdc-a32d-e0684ef8d378 · inbound

Geometric Hyena Networks for Large-scale Equivariant Learning cites this paper.

Geometric Hyena Networks for Large-scale Equivariant Learning Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 11

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no resolver link, observed 2026-08-07T13:11:41.546011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:11:41.546011Z digest=sha256:8df9e820edb6ed023e5711f626d2eacbae01fcb2cd8bbe1b0dbb42d293ae6c7d

Observation 51c5621c-26de-4fc5-a3fb-9b8fc8667510 · inbound

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training cites this paper.

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 30

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no resolver link, observed 2026-08-07T10:30:12.746450Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:30:12.746450Z digest=sha256:dd217871d73caf71d762fb9610130827afa5d613e6fc9be3ad85208d3f00753f

Observation 55da58e0-17ae-4336-a8a0-252aad8ba740 · inbound

A Survey of Retentive Network cites this paper.

A Survey of Retentive Network Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 16

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no resolver link, observed 2026-08-07T05:56:23.523266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:56:23.523266Z digest=sha256:8afba811419e6b4b482d171985691e521fb631c3361322cc32e379ac3fad6bea

Observation e10fe4ab-9031-49e5-a69c-d6f1a50e3800 · inbound

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection cites this paper.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

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no resolver link, observed 2026-08-06T23:28:44.335630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:44.335630Z digest=sha256:f222015add5318992b45d5dd12a1387055ac34bb1dd62c36999cae5c1f10e9d2

Observation ebc456d3-14e0-4f9f-ab24-06bafc44b2d6 · inbound

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent cites this paper.

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 39

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verified exact
local_arxiv, observed 2026-05-15T11:17:24.628777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T11:17:24.406028Z digest=sha256:f07ed38267640dfa93df38998c8f294af15f37cc570c3741c3d2c05d69147d70

Observation c333a267-75e7-4be8-bc80-e3cfba4385c1 · inbound

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent cites this paper.

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 39

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no resolver link, observed 2026-08-06T20:40:11.917229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:40:11.917229Z digest=sha256:afd1c0624f75241062833644387adcacd0a5e5fbedf62d18e815db5b72a5fc8a

Observation 889cdfca-77ea-444e-be11-5f1d974dba96 · inbound

AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding cites this paper.

AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 23

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no resolver link, observed 2026-08-06T20:29:49.624319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:29:49.624319Z digest=sha256:38b079d4ceddd1d75a28148bd413273337f8f5ebca4caec3fd66712a910d0898

Observation 3434b521-120a-42f8-ba19-6eb9e017bf1d · inbound

A Systematic Analysis of Hybrid Linear Attention cites this paper.

A Systematic Analysis of Hybrid Linear Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 33

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no resolver link, observed 2026-08-06T19:09:55.788354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:09:55.788354Z digest=sha256:dbac7387fecffe24a69911723d073b67be05da316de0589081b95301d124810c

Observation ad621e97-2937-4b58-bf06-027b3959d7c1 · inbound

Lizard: An Efficient Linearization Framework for Large Language Models cites this paper.

Lizard: An Efficient Linearization Framework for Large Language Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 6

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local_arxiv, observed 2026-05-19T04:42:04.534710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-19T04:37:55.034479Z digest=sha256:56e5e93e787d8a5f90f6bdcf97efa1738e13a6e1ce532de4f744a9fc75535c9f

Observation 1391d620-ee6f-4a0c-9b19-b0c38e309ee1 · inbound

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance cites this paper.

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 32

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no resolver link, observed 2026-08-06T11:44:04.925289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T11:44:04.925289Z digest=sha256:41b56247e1b79909d03d7630918e6ba8cf8f7a8c275e450392bb4b0a429d909f

Observation 3708a5fe-043a-4854-917d-3b1719bd4270 · inbound

SpikingBrain: Spiking Brain-inspired Large Models cites this paper.

SpikingBrain: Spiking Brain-inspired Large Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 7

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verified exact
local_arxiv, observed 2026-05-18T18:51:45.775697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T18:51:06.243305Z digest=sha256:1b8f8846173544c0119c76d5ad788d0df61780aca64025198b65835fa3ffcfc0

Observation 9f1ea1b3-5a5c-4b25-ba5a-f1778b03a762 · inbound

Elucidating the Design Space of Decay in Linear Attention cites this paper.

Elucidating the Design Space of Decay in Linear Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 7

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no resolver link, observed 2026-08-05T05:29:21.297289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T05:29:21.297289Z digest=sha256:01aa139f80847fb20206f8d99cae2eba202d80215dc4a95b089653f7d5a26c7c

Observation 48db92f2-b4c7-4b96-867b-744794c02828 · inbound

Short window attention enables long-term memorization cites this paper.

Short window attention enables long-term memorization Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

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verified exact
local_arxiv, observed 2026-05-18T12:11:21.808263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T12:10:42.646127Z digest=sha256:c9fe7755468b32b4088cb2dd6ec591fd1a9fbe47b8e5170f5774fe1bae45e378

Observation 82a3cfd6-46e4-4b09-877f-f83ccafb0747 · inbound

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights cites this paper.

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 11

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verified exact
local_arxiv, observed 2026-05-18T10:21:15.108907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T10:18:04.431436Z digest=sha256:6b79e5f6b05b9b6e9eec5d80a3f26fc6df3e492510909c23515ba9fb4c884f88

Observation 60ec5ed0-64e6-4810-b69a-d0a5625e074e · inbound

DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone cites this paper.

DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 10

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no resolver link, observed 2026-08-03T21:20:34.467266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T21:20:34.467266Z digest=sha256:3f81100b6427d22dcdb68b528954847677cc859eda1f2bf63a3d76cba0fbb19d

Observation 863a79ef-9659-4fb0-b88e-684dc47799f3 · inbound

Selective Rotary Position Embedding cites this paper.

Selective Rotary Position Embedding Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-05-17T20:40:14.861957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-17T20:36:49.650895Z digest=sha256:4585da3342ccd76ad2f9e245527f90b712670f98facfc8b89596c38bb646a107

Observation 499c3aa2-5ce8-4b8a-b40f-535f35f94d3b · inbound

Selective Rotary Position Embedding cites this paper.

Selective Rotary Position Embedding Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 14

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unresolved
no resolver link, observed 2026-08-03T21:03:18.981806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T21:03:18.981806Z digest=sha256:71551391c7aef5895e27b9b3dffa3822eb28d6b230eebed05d85f0187b921190

Observation af9b1816-3b38-4915-8786-0b4661921526 · inbound

Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression cites this paper.

Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 11

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verified exact
local_arxiv, observed 2026-05-21T18:00:27.115409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T17:59:23.826110Z digest=sha256:b02b32b2336c2195004f0337fc1423cb7348877f7946bdd934c266f6b07f2824

Observation 6f226ae0-8f0e-4025-88d8-a9f70e0c8272 · inbound

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers cites this paper.

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 20

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no resolver link, observed 2026-08-03T15:22:48.561819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T15:22:48.561819Z digest=sha256:63989ea583b646c274940131622e0f5859570cc631773cc5837175b0095b0a89

Observation 3b83e026-48db-4285-9fa0-0126f68688f8 · inbound

Distill-then-Replace: Efficient Task-Specific Hybrid Attention Model Construction cites this paper.

Distill-then-Replace: Efficient Task-Specific Hybrid Attention Model Construction Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:12:43.396788Z digest=sha256:accc1f5a94d3c80e2abf06a03b9cfb236d3e2980c19fd5a32a3c6a1272ccf6b4

Observation 18a0e62c-33ee-474a-9528-d0cb10165e65 · inbound

When to Think Fast and Slow? AMOR: Adaptive Entropy Gate for Hybrid Models cites this paper.

When to Think Fast and Slow? AMOR: Adaptive Entropy Gate for Hybrid Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 6

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local_arxiv, observed 2026-05-16T11:50:52.802578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-16T11:48:50.587732Z digest=sha256:7184f6a0939a903573c7bdec530659f832796ebbbca7f724aaefe81fd5ffd20f

Observation 38ac6792-76ff-4180-82a3-fead463c9dcf · inbound

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference cites this paper.

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

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metadata mismatch
local_arxiv, observed 2026-05-15T21:00:17.897130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T20:59:33.902420Z digest=sha256:4a28195f33a8e82175848eb6136aa02a105104c1240d505f2b3a524e5a86fe07

Observation d4c25bb4-d24c-44a4-a2f9-991ef4125905 · inbound

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference cites this paper.

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

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metadata mismatch
local_arxiv, observed 2026-05-21T12:50:09.519771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T12:45:27.150368Z digest=sha256:91f9b5117131b373e34b459c14c91255bcf68a6885ef58a621ce68fa6f6fd820

Observation 98aafb65-e4c0-4f2a-9a27-cbbbbc0b0d3d · inbound

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference cites this paper.

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2022

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no resolver link, observed 2026-08-02T22:05:42.987805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:05:42.987805Z digest=sha256:fecc61e6884de6fd43a47e53b2cc5bd94ca3d363533f68b5aabb98884ab32886

Observation daa7580c-ff19-4d6f-8931-76acb0864547 · inbound

When Does Content-Based Routing Work? Representation Requirements for Selective Attention in Hybrid Sequence Models cites this paper.

When Does Content-Based Routing Work? Representation Requirements for Selective Attention in Hybrid Sequence Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 19

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verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T06:24:41.108227Z digest=sha256:58aea7ec95aa072700dbc3556923bbd0c2736f4c6dff3e34bc7d1a99b0ec9601

Observation 50eab17c-36ae-4490-acd5-02b0dccfdb7d · inbound

When Perplexity Lies: Generation-Focused Distillation of Hybrid Sequence Models cites this paper.

When Perplexity Lies: Generation-Focused Distillation of Hybrid Sequence Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:21:21.451268Z digest=sha256:35a35b5b25db42ce85e6fa8e1bb6c7be768af8685c62cdcdfd64b42788f80325

Observation 92a3e1eb-c599-41c1-a96a-74bd0879067a · inbound

LPC-SM: Local Predictive Coding and Sparse Memory for Long-Context Language Modeling cites this paper.

LPC-SM: Local Predictive Coding and Sparse Memory for Long-Context Language Modeling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2

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verified exact
local_arxiv, observed 2026-05-15T11:25:31.080573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T11:22:08.937342Z digest=sha256:4bb1b397b75dbd7afea74d8782228a3a353e211fb9bb5342e224666768262dc3

Observation e0b7d0a9-90b9-4854-b824-0dab5b38ea57 · inbound

Mambalaya: Einsum-Based Fusion Optimizations on State-Space Models cites this paper.

Mambalaya: Einsum-Based Fusion Optimizations on State-Space Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 18

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arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-13T16:55:36.438348Z digest=sha256:806dd90d13cd4cf5d9b0ef4e358fe1fc7ad1b1534cd6d77331d43766ab877303

Observation df3bb33e-85ce-467e-a284-af7124e56847 · inbound

Mambalaya: Einsum-Based Fusion Optimizations on State-Space Models cites this paper.

Mambalaya: Einsum-Based Fusion Optimizations on State-Space Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 17

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no resolver link, observed 2026-08-02T16:51:07.301614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:51:07.301614Z digest=sha256:858c86206c599c44f81541cf41d83f6a5e15d33c0143ff5c0c093c38be5db1a3

Observation 9cdce8f7-b0c3-485f-ade1-7cdee9c524c2 · inbound

CAWN: Continuous Acoustic Wave Networks for Autoregressive Language Modeling cites this paper.

CAWN: Continuous Acoustic Wave Networks for Autoregressive Language Modeling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

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no resolver link, observed 2026-07-13T10:31:11.574398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T10:31:11.574398Z digest=sha256:ba91f79bb640e419c15874e6a33a840331631c7ff50a8442993e5d5d6f7b63d8

Observation 6400ba6b-3a57-4e36-9564-fd52ac10af70 · inbound

Phase-Associative Memory: Sequence Modeling in Complex Hilbert Space cites this paper.

Phase-Associative Memory: Sequence Modeling in Complex Hilbert Space Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 101

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arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T19:39:50.311059Z digest=sha256:34682b0c389ed4ba1714afaf42e456cdc55fc09c984d3bb32edf0f44390baafe

Observation 701eff04-23f3-44ac-b058-ca11003e9a80 · inbound

Optimal Decay Spectra for Linear Recurrences cites this paper.

Optimal Decay Spectra for Linear Recurrences Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 4

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verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T17:21:15.178258Z digest=sha256:ece9cd8c4a06a80cac0ae13fe96a295cb82a4e3fb3c37e3c4d1ebaa9681e8849

Observation a32ec29c-a010-4964-94b3-eecb39f70070 · inbound

TAPNext++: What's Next for Tracking Any Point (TAP)? cites this paper.

TAPNext++: What's Next for Tracking Any Point (TAP)? Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 6

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verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T15:29:15.631211Z digest=sha256:a42b47b03fe89b4696527404d8af14537bbd52a73fd95cd6b2b9e9ffa997b237

Observation a45c8722-7873-4785-876a-8a40cd86a7dd · inbound

On the Expressive Power and Limitations of Multi-Layer SSMs cites this paper.

On the Expressive Power and Limitations of Multi-Layer SSMs Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 1

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arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T12:36:09.265655Z digest=sha256:d6644350c36038e0ef1c68581d86004546dfa54fe88fa374ca55018ff9eace5e

Observation 56d86a29-2489-4db4-b253-f5d44635e2d1 · inbound

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification cites this paper.

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 13

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verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T01:10:45.859345Z digest=sha256:d1d5d2cd56d9e7fbe90753d7fada30abab58f07f19c026d27dc6efda13deae6b

Observation 75eff084-6ebd-43c0-856c-502724664f30 · inbound

HubRouter: A Pluggable Sub-Quadratic Routing Primitive for Hybrid Sequence Models cites this paper.

HubRouter: A Pluggable Sub-Quadratic Routing Primitive for Hybrid Sequence Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T12:21:07.816749Z digest=sha256:bcbc5ea8f42a25e13f602437d4898a6fe6e7b9ae6169f6e9299d95db66b3735b

Observation ddd9849a-3219-4fcf-b569-e3919cb12db7 · inbound

SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference cites this paper.

SpikingBrain2.0: Brain-Inspired Foundation Models for Efficient Long-Context and Cross-Platform Inference Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T12:18:23.898779Z digest=sha256:951e9f764bb5e5e6b18c96077a909a0308fa6fe91287bc3edf29c55c69123be9

Observation 089576dd-8998-4e0e-a175-7547cbf926bf · inbound

The Impossibility Triangle of Long-Context Modeling cites this paper.

The Impossibility Triangle of Long-Context Modeling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T17:17:44.033300Z digest=sha256:dad567990e79ff43b9e039464066ca5ac0760ded3b10daf2c234298f9274a9bc

Observation 7340369a-81bb-4691-9c29-ce1f099bc359 · inbound

How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences cites this paper.

How Long Does Infinite Width Last? Signal Propagation in Long-Range Linear Recurrences Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T17:52:21.272304Z digest=sha256:e18a1b90a32bfa3176f600bc6721cd70c65cea26cceb64c89444735c7b39e124

Observation 4377f820-c8ca-440a-83e0-675a877b3276 · inbound

A Robust Foundation Model for Conservation Laws: Injecting Context into Flux Neural Operators via Recurrent Vision Transformers cites this paper.

A Robust Foundation Model for Conservation Laws: Injecting Context into Flux Neural Operators via Recurrent Vision Transformers Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-08T16:52:57.496945Z digest=sha256:8229b899972c5c84bf9b7014c1b30641e5b1e58542a3f8b095997206b5fc7ee6

Observation 64cc7df7-1c6f-4b56-b12a-afdc3eb9d202 · inbound

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

MDN: Parallelizing Stepwise Momentum for Delta Linear Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-09T15:27:55.566795Z digest=sha256:bab1d64735b8ffa525456fc5dc354dca6596753f84af0cd8ad20426dfa7b4331

Observation 96e8e322-c921-43a0-b314-a6df5715f1a1 · inbound

Priming: Hybrid State Space Models From Pre-trained Transformers cites this paper.

Priming: Hybrid State Space Models From Pre-trained Transformers Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-12T01:14:01.584159Z digest=sha256:92c21e87540cd8121fdfd736979107aae4857ffa8fd6cb091c43d9716e3f38e6

Observation 790946f8-6032-4a68-b1ba-7269b092e9e3 · inbound

Kaczmarz Linear Attention cites this paper.

Kaczmarz Linear Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-12T01:15:58.330766Z digest=sha256:1d04e00245a8dca7e002f6c7d841d1a9c4555e01f1d3ab8744a9cdc7f9dc5469

Observation 401870ed-087a-44c5-b4b1-d6c4e4c9aeef · inbound

MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading cites this paper.

MemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-12T05:22:43.330891Z digest=sha256:21170972a078c65c764ab6120232986624976bc634cea57343e8a0e9e14893cb

Observation 9dfb6fda-8d60-4f2c-b257-bcb8a0fa20ed · inbound

A Single-Layer Model Can Do Language Modeling cites this paper.

A Single-Layer Model Can Do Language Modeling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T06:58:17.635264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-12T04:40:12.906234Z digest=sha256:32735e2ce7f7a0d7cf554c696e1d554ee1c96f72d20a996e172ab3ca080d0656

Observation 83d8a01a-56d0-4642-b16b-1c7036365860 · inbound

HexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities cites this paper.

HexagonalWarriorMamba: Superior Threshold-Dependent Multi-label Classification of 12-Lead ECG Cardiac Abnormalities Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-05-20T11:58:14.929076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T11:55:41.887085Z digest=sha256:56009c4a7635f4eb7136a2212806f2a46452cfb598b1130328692f3d5a0f58c6

Observation e1fae2d7-c4ea-4e00-96f4-2c0b0b29b0dd · inbound

The Routing and Filtering Structure of Attention cites this paper.

The Routing and Filtering Structure of Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-05-20T22:09:07.720892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T22:04:15.518405Z digest=sha256:9aae522a65b15e4af46f4d364aebd4f926163d243457afbeb7993068db25f1f3

Observation 0c87302f-66bf-4d22-bba4-ee4092b054db · inbound

Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models cites this paper.

Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 12

Resolution
metadata mismatch
local_arxiv, observed 2026-05-20T11:53:15.168578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T11:48:42.834602Z digest=sha256:68f51227c48c12db672a3caf7219a3e4d061d26434fcb0278c981ce22a687d61

Observation 4aa09321-2184-4788-ac6f-37eaa4c3edd2 · inbound

Towards Understanding Self-Pretraining for Sequence Classification cites this paper.

Towards Understanding Self-Pretraining for Sequence Classification Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 101

Resolution
metadata mismatch
local_arxiv, observed 2026-05-21T05:33:58.868560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-21T05:29:58.809024Z digest=sha256:aeabe7fc02a49836117218b388b76c0baf0ed0f37efa02e8874e1100f9bc8478

Observation e4cfe493-1848-4837-9464-cef8867d4bcf · inbound

Interdomain Attention: Beyond Token-Level Key-Value Memory cites this paper.

Interdomain Attention: Beyond Token-Level Key-Value Memory Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2

Resolution
metadata mismatch
local_arxiv, observed 2026-06-30T14:04:44.092256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-07-11T11:50:26.030339Z digest=sha256:4abdb9bcc07204e3f4ee579ae14f507dd9a56f179baffc7dc1836d25620de2d3

Observation 9fab381a-a2bf-4a65-9094-474dbe26edb4 · inbound

Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference cites this paper.

Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-06-29T21:43:59.493853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-29T21:37:51.638904Z digest=sha256:327c40199f6b792ac15b7b34f12988d4ca173a29632bfacc0469abe81ece70f1

Observation 2efbb355-3685-403c-8cb5-003ce1c758cb · inbound

Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior cites this paper.

Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-06-29T19:43:54.937865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-29T19:36:13.559393Z digest=sha256:602df6614b482b7a47e98dfb5ecd7a680de8908104c36e37765c254a860650d3

Observation e3f096b0-1cbd-4357-b8e1-626a9d28993d · inbound

Memory by Design: Probabilistic Sequence Layers cites this paper.

Memory by Design: Probabilistic Sequence Layers Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T20:26:12.920120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-28T21:07:31.407554Z digest=sha256:a1c90bff07534f6e5314abb8c0ba382fa708d3216bc849ec27776513bf2bb767

Observation 48ca4380-ab64-4ea9-aab1-9d50ceb66ddb · inbound

Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing cites this paper.

Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 1

Resolution
metadata mismatch
local_arxiv, observed 2026-06-28T23:02:45.801805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-07-11T11:50:26.030339Z digest=sha256:844a56c9bf6ac2bcd2e9cb652a83dd458bae1ec28990ca1e44210ab22cc4ef92

Observation 88cd020b-e37e-4376-b097-65ad34fab426 · inbound

Forget Attention: Importance-Aware Attention Is All You Need cites this paper.

Forget Attention: Importance-Aware Attention Is All You Need Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T23:06:21.029340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-28T14:38:40.948032Z digest=sha256:2a48a3e1369b75251b78a9b853395650e3a1215eeebfefcb399da32a634ecfb4

Observation 02f27307-df19-4fa0-8a6a-6ce6cce7549b · inbound

LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling cites this paper.

LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 42

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T06:36:44.182382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-28T07:19:31.075298Z digest=sha256:7de0e550b79d148941ed5afaa16db01bf29d0fe035aaf0b46f7c2e5bd0bd4369

Observation 324b4bc8-0224-4646-9c55-f236ee2be205 · inbound

Titans-as-a-Layer: Test-Time Memory for Conversational Speech Emotion Recognition cites this paper.

Titans-as-a-Layer: Test-Time Memory for Conversational Speech Emotion Recognition Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T23:07:26.839436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T18:28:44.652813Z digest=sha256:2d2a3d8300c40830770b729891ca1f01aa9881d9b20e2254ac4bce8f2f7a8526

Observation b25d24ce-a02b-48a6-b3bd-039a3bc986c2 · inbound

Blurry Window Attention cites this paper.

Blurry Window Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-07-01T20:46:14.017951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-28T17:43:34.429061Z digest=sha256:aa7ad45ec6dda607ed93512be2afbff4a198bae3c163d18aa20f63aee0cf010e

Observation a25b4ef6-89f8-44d7-b979-031b11ffa75b · inbound

Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees cites this paper.

Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-06-27T12:10:53.818017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T12:08:57.506333Z digest=sha256:b5857561380e7021a6cf9698a9cfbb86e704313380370842a94d114f06256102

Observation e1a196cd-9290-4ccc-96b3-02e2ec849791 · inbound

PLUME: Probabilistic Latent Unified World Modeling and Parameter Estimation for Multi-Finger Manipulation cites this paper.

PLUME: Probabilistic Latent Unified World Modeling and Parameter Estimation for Multi-Finger Manipulation Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-07-03T06:17:41.379799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-27T12:50:32.341334Z digest=sha256:0dd4bde153d645283011c1e5d083980600da04e8b8bff4843a6607cf09f33c1d

Observation 63a05911-a45c-4fd6-a0f0-b66700b73686 · inbound

Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning cites this paper.

Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 23

Resolution
metadata mismatch
local_arxiv, observed 2026-07-03T09:47:59.882874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-27T10:18:54.163862Z digest=sha256:3ab1f921f5b7bd794dbad8272751f7ddc4b3f2445d2635292f93ebbbb43c1e8d

Observation cef66bdb-d22a-40e8-a56b-ae62af49193e · inbound

Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution cites this paper.

Linear Recurrent Unit with Semantic Modulation for Image Super-Resolution Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-07-04T03:29:29.435897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T18:06:22.521551Z digest=sha256:ba196dedceb686ebe4f4707bf382fbea8fe03eca8ff0d287ca05cb737f9654f6

Observation 56711364-b5c6-438f-b08b-655bbb5d52a3 · inbound

Tapered Language Models cites this paper.

Tapered Language Models Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-07-04T09:59:45.305176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T09:11:20.341634Z digest=sha256:ed7418b37af2bcf574021094083a92c3c7b155f25b770445c620fd86728bb571

Observation df893389-7fa6-4dbe-bedc-a57a55a99500 · inbound

Harmonic: Hierarchical State Space Models for Efficient Long-Context Language Modeling cites this paper.

Harmonic: Hierarchical State Space Models for Efficient Long-Context Language Modeling Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-06-28T19:42:35.933085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-28T18:55:19.253621Z digest=sha256:7a98cc3d4978b344d995eb5e97153a80b97d7e28537f91608c51daef19f007ed

Observation fdc4b206-788c-4312-b5af-077c67d64a90 · inbound

SSM Adapters via Hankel Reduced-order Modeling: Injection Site Determines Task Suitability in Long-Context Fine-Tuning cites this paper.

SSM Adapters via Hankel Reduced-order Modeling: Injection Site Determines Task Suitability in Long-Context Fine-Tuning Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 1

Resolution
metadata mismatch
local_arxiv, observed 2026-07-04T15:19:56.025588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T01:46:44.408246Z digest=sha256:c62425e736734f29e0c1e64328671a1191dab0a63708026d243d09bfa7ca5e03

Observation c128b580-bcc7-4126-b36b-bf9276309c66 · inbound

CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention cites this paper.

CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-07-04T14:09:53.441851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T04:26:00.066003Z digest=sha256:c2c4f81faa1fb909b6f02e95629e3547f2418176fe826dc7b078d3afaeb8febf

Observation 2f81bd82-e038-42cd-a18e-3791e2cff5fd · inbound

CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention cites this paper.

CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-06-30T09:44:37.449959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-30T09:42:52.096671Z digest=sha256:4ec0639fd90ba39f022ab54c65f326d424e7fee5a082fabf6a24f740cb1b0342

Observation 21955ff2-0e38-425b-9bf5-0f884c42ab49 · inbound

CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention cites this paper.

CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

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The Context-Ready Transformer cites this paper.

The Context-Ready Transformer Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 20

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