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Paper Citation Record · LEDGER

HGRN2: Gated Linear RNNs with State Expansion

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2404.07904.

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

pith.paper-citation-record.v1
2404.07904 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 32 of 32 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:17:29.421987Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:28:44.977281Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 028141c2-506e-482e-9706-b95408296bc9 · inbound

Gated Linear Attention Transformers with Hardware-Efficient Training cites this paper.

Gated Linear Attention Transformers with Hardware-Efficient Training HGRN2: Gated Linear RNNs with State Expansion

Reference 75

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arxiv_id, observed 2026-05-15T01:15:14.178481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-15T01:15:13.991219Z digest=sha256:4b28ffcbd72c60cc721dad378be47be449350ebf41f5c3e66f97a24a2371ca73

Observation 3180dd9b-e417-4b83-bb09-9c7aee3395a6 · 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 HGRN2: Gated Linear RNNs with State Expansion

Reference 84

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verified exact
arxiv_id, observed 2026-05-11T12:16:25.924691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation cdfc1606-882b-4c46-9e3a-584d247c356c · inbound

State Space Models are Strong Text Rerankers cites this paper.

State Space Models are Strong Text Rerankers HGRN2: Gated Linear RNNs with State Expansion

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T12:22:28.467675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bb9dc605-5b32-4362-95c2-dfc742b0af19 · 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 HGRN2: Gated Linear RNNs with State Expansion

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-10T22:52:33.568763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:52:33.568763Z digest=sha256:a7228433b28f41f20ed6749359e73aa8f4dfada374ec5fd53a6bec7278b64c44

Observation 1bd148f1-0b79-4d3e-b370-2b16168f3bca · inbound

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

An Uncertainty Principle for Linear Recurrent Neural Networks HGRN2: Gated Linear RNNs with State Expansion

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T22:10:15.909661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T22:10:15.909661Z digest=sha256:a1f549eb921977ef9be410f63ce8f84c05bad3c6770328d3f8597edc92e08878

Observation 53e3cf3f-bd85-4455-9a3e-65a41694f548 · inbound

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 cites this paper.

Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2 HGRN2: Gated Linear RNNs with State Expansion

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-08T10:59:50.623523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T10:59:50.623523Z digest=sha256:8783d0c579bc63091de4f1db4077c2c90aa3257a72a83c2ab66977ae10ffb2e6

Observation 4d21a790-bd48-41cf-b027-ef278a9b576c · inbound

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism cites this paper.

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism HGRN2: Gated Linear RNNs with State Expansion

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-16T11:17:29.421987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:17:29.421987Z digest=sha256:da5d51665fbfa49d0324986cebf831d8ee20cbd93b9cf89a970a52b5e322e4a8

Observation 7842a8f4-3636-4956-99e9-e157b6e0d9e1 · inbound

Overflow Prevention Enhances Long-Context Recurrent LLMs cites this paper.

Overflow Prevention Enhances Long-Context Recurrent LLMs HGRN2: Gated Linear RNNs with State Expansion

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-15T22:12:58.798549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:12:58.798549Z digest=sha256:7fd1e45fee2669d6ec7836f2f3771f1d31c2d22b77eb4630d764525f8839ef03

Observation 573992a7-f2eb-43aa-b04e-c85c2dff66dd · inbound

Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective cites this paper.

Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective HGRN2: Gated Linear RNNs with State Expansion

Reference 25

Resolution
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no resolver link, observed 2026-08-06T20:53:49.949048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:53:49.949048Z digest=sha256:5f9546ac4da3e17a4f455798f75e74e0f28604b371230f4a1166a1f78ef73a16

Observation cd664a8c-0092-49a1-b441-3ec3454ef6ee · inbound

A Survey on Latent Reasoning cites this paper.

A Survey on Latent Reasoning HGRN2: Gated Linear RNNs with State Expansion

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-06T19:14:29.778788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:14:29.778788Z digest=sha256:e5a8b0208aa7cc9716bec0f339a1e247dd9858aff8f618723a02ccc880e0ba53

Observation 6f9b6f7c-42b2-4d19-9f4f-fb6d6cc589ad · inbound

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

Elucidating the Design Space of Decay in Linear Attention HGRN2: Gated Linear RNNs with State Expansion

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T05:29:21.596988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T05:29:21.596988Z digest=sha256:63bd10d45ada39b31d25929dd0e44cb543b67b35253d4f93c0d9a7453e7f2a95

Observation 4b894af1-4bf3-4a35-8a49-af66b2722404 · inbound

Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism cites this paper.

Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism HGRN2: Gated Linear RNNs with State Expansion

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-18T03:05:48.046309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2fc4299c-e2db-4779-aba3-9f49db45f35f · inbound

Kimi Linear: An Expressive, Efficient Attention Architecture cites this paper.

Kimi Linear: An Expressive, Efficient Attention Architecture HGRN2: Gated Linear RNNs with State Expansion

Reference 78

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verified exact
arxiv_id, observed 2026-05-13T23:49:10.756376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 4fbf30cf-0485-4cbc-ab7b-b2ff3e879222 · inbound

Selective Rotary Position Embedding cites this paper.

Selective Rotary Position Embedding HGRN2: Gated Linear RNNs with State Expansion

Reference 51

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verified exact
arxiv_id, observed 2026-05-17T20:40:14.876027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-17T20:36:49.650895Z digest=sha256:e2d886b4fd95a02fb9aad60733891cb93d805b58b192c0a6085c752cc1143274

Observation 57230f3d-9378-40cb-8c2f-cebced81accd · inbound

Selective Rotary Position Embedding cites this paper.

Selective Rotary Position Embedding HGRN2: Gated Linear RNNs with State Expansion

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-03T21:03:22.534290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T21:03:22.534290Z digest=sha256:d62f6a3bc2e480b51cffe9d9e968338befb953fa71dbb88102bf908bcbd251dd

Observation 2462ea1a-cdd9-4d42-aa05-ab26cce44ba9 · inbound

Test-Time Training with KV Binding Is Secretly Linear Attention cites this paper.

Test-Time Training with KV Binding Is Secretly Linear Attention HGRN2: Gated Linear RNNs with State Expansion

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-15T19:41:32.655604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 256821c7-39ac-4d26-bc67-d7e1a707ad25 · inbound

Attention Residuals cites this paper.

Attention Residuals HGRN2: Gated Linear RNNs with State Expansion

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:39:04.421192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation f09d6923-58ed-4883-a640-16d130ffb17e · inbound

FG$^2$-GDN: Enhancing Long-Context Gated Delta Networks with Doubly Fine-Grained Control cites this paper.

FG$^2$-GDN: Enhancing Long-Context Gated Delta Networks with Doubly Fine-Grained Control HGRN2: Gated Linear RNNs with State Expansion

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:01:23.745730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 09aa5059-09a1-4359-aefd-d3ac460fc2ea · inbound

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

The Impossibility Triangle of Long-Context Modeling HGRN2: Gated Linear RNNs with State Expansion

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:41:08.965747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3d723a27-c177-4a9d-bf34-390fced108ca · inbound

Cubit: Token Mixer with Kernel Ridge Regression cites this paper.

Cubit: Token Mixer with Kernel Ridge Regression HGRN2: Gated Linear RNNs with State Expansion

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:06:10.811375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-08T12:38:19.925573Z digest=sha256:2bbdff0d7fa41819a7617012610f82c905bc0da5cc4896a21eb42aaca9fde3fe

Observation c8242dce-7d6d-41ea-ac26-ad571e9917a8 · inbound

Cubit: Token Mixer with Kernel Ridge Regression cites this paper.

Cubit: Token Mixer with Kernel Ridge Regression HGRN2: Gated Linear RNNs with State Expansion

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:39:11.022749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 286fdac0-1d10-48d5-83b3-abbc37c78f17 · inbound

Elastic Attention Cores for Scalable Vision Transformers cites this paper.

Elastic Attention Cores for Scalable Vision Transformers HGRN2: Gated Linear RNNs with State Expansion

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:07:22.515060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 11cc2d6a-49c9-4f2f-95bf-a83671a01053 · inbound

SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting cites this paper.

SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting HGRN2: Gated Linear RNNs with State Expansion

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-14T02:18:37.510692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2a073cd0-b19c-4d8e-9003-5973ba72f6e0 · inbound

LT2: Linear-Time Looped Transformers cites this paper.

LT2: Linear-Time Looped Transformers HGRN2: Gated Linear RNNs with State Expansion

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:13:59.615527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 7fada328-71dc-4991-8818-72c4914f5af7 · inbound

LT2: Linear-Time Looped Transformers cites this paper.

LT2: Linear-Time Looped Transformers HGRN2: Gated Linear RNNs with State Expansion

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-06-30T17:54:57.816696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2af5326e-6c1c-4c7a-8052-81474fca269d · inbound

Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention cites this paper.

Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention HGRN2: Gated Linear RNNs with State Expansion

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-22T04:54:36.620527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-22T04:53:24.400091Z digest=sha256:690ef13a330f19ec35d7845fffbb65d917d926e48b58f73d907e34afca331d5f

Observation 7bf3a3f5-a8fe-4aaa-8b4e-07f072b5ffef · inbound

Universal Time Series Generation with Neural Controlled Differential Equations cites this paper.

Universal Time Series Generation with Neural Controlled Differential Equations HGRN2: Gated Linear RNNs with State Expansion

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-06-29T14:13:30.017761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 06e63161-e187-4f3d-b004-5d4da99d1b39 · inbound

Memory by Design: Probabilistic Sequence Layers cites this paper.

Memory by Design: Probabilistic Sequence Layers HGRN2: Gated Linear RNNs with State Expansion

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-01T20:26:12.859084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-28T21:07:31.407554Z digest=sha256:b6975b550cb02e4cbddb5f4df565e9707930573cc6662945242b8e458c5293c7

Observation c413e580-7b36-4bea-888e-fe0e4cf2d629 · inbound

Dynamic Short Convolutions Improve Transformers cites this paper.

Dynamic Short Convolutions Improve Transformers HGRN2: Gated Linear RNNs with State Expansion

Reference 174

Resolution
verified exact
arxiv_id, observed 2026-07-02T02:36:27.001391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-28T10:48:50.103004Z digest=sha256:d29e0d912fcc3216d49babab234feca6ce1014711c2a6f9759d31788cd0d1e55

Observation a1d64929-c85f-4d00-a050-bcb460179fa0 · 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 HGRN2: Gated Linear RNNs with State Expansion

Reference 76

Resolution
verified exact
arxiv_id, observed 2026-07-03T09:47:59.839072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-27T10:18:54.163862Z digest=sha256:b2b6dee678810df85735d1b3fc7bbd5e50f9c9dfe90fa6c336ee57cbf4002e46

Observation 4f22a659-6728-4a2c-a2ea-e049194f67f6 · inbound

Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI cites this paper.

Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI HGRN2: Gated Linear RNNs with State Expansion

Reference 185

Resolution
verified exact
arxiv_id, observed 2026-07-03T17:28:44.978700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-27T04:02:53.110012Z digest=sha256:f3474d9c9a2ab05116ec6db4c73ba6dba8df9cd53420781e29b9f78e1a28afbd

Observation 7d24fd88-1556-4edf-a88b-f2fd67fe5c67 · inbound

Morphing into Hybrid Attention Models cites this paper.

Morphing into Hybrid Attention Models HGRN2: Gated Linear RNNs with State Expansion

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:44:28.077887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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