Paper Citation Record · LEDGER

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

As of 7 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 88 inbound Pith citation observations for arXiv:2505.06708.

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

pith.paper-citation-record.v1
2505.06708 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-12T09:04:34.807225Z

measured 123 of 123 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 88 of 88 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:39:48.142895Z

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

35 of 35 outbound references displayed

verified exact23
verified fuzzy2
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch10

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 97912554-94d4-4cf7-936e-6d34bcef2eff · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.858173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 70532bf2-c7fa-43a7-979c-e5295f1f2c6d · outbound

This paper cites Numerical stability analysis of large language models.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Numerical stability analysis of large language models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-23T04:13:38.174619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64175e53-3209-47b6-a2ce-246e35f1ec77 · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Extending Context Window of Large Language Models via Positional Interpolation

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T10:20:58.016961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dcd9a6e4-6f5c-4bf0-a7ce-8ed1ce327d3e · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Training Verifiers to Solve Math Word Problems

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.842259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c26bf0d3-ad0a-47c4-97eb-d68aea22306b · outbound

This paper cites Approximating Two-Layer Feedforward Networks for Efficient Transformers.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Approximating Two-Layer Feedforward Networks for Efficient Transformers

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.846334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c149812-9858-4e70-91db-36b473469cd7 · outbound

This paper cites MoEUT: Mixture-of-Experts Universal Transformers.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free MoEUT: Mixture-of-Experts Universal Transformers

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.850063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2f11968f-fa69-4f79-babf-1adebdd2b943 · outbound

This paper cites Transformers are ssms: Generalized models and efficient algorithms through structured state space duality.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Transformers are ssms: Generalized models and efficient algorithms through structured state space duality

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T09:04:34.962135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94c1d6b0-5792-40ac-b6f9-58627ad7fd48 · outbound

This paper cites Vision Transformers Need Registers.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Vision Transformers Need Registers

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T09:41:38.514155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 617e6ebe-0213-471a-8ec9-013f266d4bf0 · outbound

This paper cites LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.866219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b44d1d2f-d7dd-492f-ac60-379d5e772c64 · outbound

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

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.870188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de9a4bb3-5fb6-436a-8f8b-80e4143c84eb · outbound

This paper cites When Attention Sink Emerges in Language Models: An Empirical View.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free When Attention Sink Emerges in Language Models: An Empirical View

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:41:03.919229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05b93118-90e1-483c-89b1-40af340288df · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Measuring Massive Multitask Language Understanding

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.877515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1ccb3e3-005f-4567-bd79-499455a8460d · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.881436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c3ed7834-f983-4826-bc1a-3e73dfe4c419 · outbound

This paper cites Transformerqualityinlineartime.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Transformerqualityinlineartime

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T09:04:34.964287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a6cc82a0-b045-4130-9a71-655fe31388d5 · outbound

This paper cites MiniMax-01: Scaling Foundation Models with Lightning Attention.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free MiniMax-01: Scaling Foundation Models with Lightning Attention

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:26:38.921226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24b7b178-4ab5-4b8d-aec8-1aa39c8c63bd · outbound

This paper cites Forgetting Transformer: Softmax Attention with a Forget Gate.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Forgetting Transformer: Softmax Attention with a Forget Gate

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.889061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd67d55e-370b-4374-8d24-76414fd3706a · outbound

This paper cites An Empirical Model of Large-Batch Training.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free An Empirical Model of Large-Batch Training

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.893990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation efaeabb2-500c-4141-864c-d9fbe3185454 · outbound

This paper cites On the Number of Linear Regions of Deep Neural Networks.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free On the Number of Linear Regions of Deep Neural Networks

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T18:57:14.932026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c35a7ac-f334-4fdf-9dd6-065b7503fd47 · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free YaRN: Efficient Context Window Extension of Large Language Models

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.900598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f65d681-9848-4316-9905-dd2d8b9b35f1 · outbound

This paper cites Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Mixture of Sparse Attention: Content-Based Learnable Sparse Attention via Expert-Choice Routing

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.904141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8f5c733-853d-4c28-a2db-c92d08ce8e6e · outbound

This paper cites Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.915567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05dd7250-bd48-4eef-a839-f1e487a147dd · outbound

This paper cites GLU Variants Improve Transformer.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free GLU Variants Improve Transformer

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.919777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7cbaa56c-e684-4749-a761-579a18b95362 · outbound

This paper cites Highway Networks.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Highway Networks

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.923850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d42df7f-ea28-419d-9a80-f2464a4d062b · outbound

This paper cites Massive Activations in Large Language Models.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Massive Activations in Large Language Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:02:54.298502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79e76366-d832-4e86-9e1c-289ba9e8afab · outbound

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

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Retentive Network: A Successor to Transformer for Large Language Models

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T09:04:34.930845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5a9bf81-705b-4e08-9e2b-4520bd8542ea · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.934454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1019b78-1466-4b3d-8e4a-39007fabba98 · outbound

This paper cites DeepNet: Scaling Transformers to 1,000 Layers.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free DeepNet: Scaling Transformers to 1,000 Layers

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.938109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81502e10-2afe-45ec-94b5-d4e3cb6b6a60 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Efficient Streaming Language Models with Attention Sinks

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.942755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a79c627-7562-4cf6-8414-0a901aaa0ed2 · outbound

This paper cites Qwen2.5 Technical Report.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Qwen2.5 Technical Report

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.945837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d78de024-8941-40d9-92e2-15d09c2c8a94 · outbound

This paper cites Interpreting the Repeated Token Phenomenon in Large Language Models.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Interpreting the Repeated Token Phenomenon in Large Language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.949399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3604fff3-b87e-4ea7-8b33-de05a245e1d4 · outbound

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

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:46:30.294205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dc9c4237-363d-4e59-bee0-a771280720a9 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.956066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 76bb52cf-5d11-492c-ae4e-e61290ed09c5 · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free GLM-130B: An Open Bilingual Pre-trained Model

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:41:34.452877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e8d7945-0255-4df4-9709-245e1884b6a1 · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T23:14:26.471193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 51a088e9-3839-42ae-b99a-df800b24805a · outbound

This paper cites Softpick: No Attention Sink, No Massive Activations with Rectified Softmax.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free Softpick: No Attention Sink, No Massive Activations with Rectified Softmax

Reference 36

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T09:04:34.829561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 38b63a25-bb82-4f23-8143-0b191005e0bd · inbound

TTT3R: 3D Reconstruction as Test-Time Training cites this paper.

TTT3R: 3D Reconstruction as Test-Time Training Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-17T06:41:16.440866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf83668c-6f1c-4e1e-b0be-bd887d7e2282 · inbound

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention cites this paper.

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-18T10:02:31.643403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab33c231-c8c6-4188-ab12-2c8a418ee5e9 · inbound

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

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-18T10:21:14.965889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation afd76c00-2e1e-4de2-801c-17f45045ef73 · inbound

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

Kimi Linear: An Expressive, Efficient Attention Architecture Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 82

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T23:49:10.768666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ddb7f6e-22a5-431d-84a9-3a008af0c8fb · inbound

BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding cites this paper.

BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:21:18.639195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ae3f44f5-b7d3-4d9d-9280-1e0bdf357842 · inbound

MiMo-V2-Flash Technical Report cites this paper.

MiMo-V2-Flash Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-12T11:33:32.874392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd6978a6-ed24-44cc-96e2-8d954dd1520d · inbound

Attention Projection Mixing with Exogenous Anchors cites this paper.

Attention Projection Mixing with Exogenous Anchors Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 708

Resolution: unresolved; no resolver link, observed 2026-08-03T10:57:26.264016Z

Source-reported events for the cited work

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Observation 62f552bd-66e9-403d-af85-493a44296a90 · inbound

Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection cites this paper.

Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T08:07:43.687328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8273196e-a848-4431-a44d-563b51eb96b0 · inbound

SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm cites this paper.

SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-22T11:14:47.997394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc7e5712-081f-496e-ab5d-09fa68e655b8 · inbound

Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling cites this paper.

Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-25T06:45:26.139781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e40f0476-06c8-47ea-bbf9-b5e0abf10be6 · inbound

Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns cites this paper.

Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:10:15.788667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 60ae144e-dee4-40bb-893b-94e07609f059 · inbound

Gated Differential Linear Attention: A Linear-Time Decoder for High-Fidelity Medical Segmentation cites this paper.

Gated Differential Linear Attention: A Linear-Time Decoder for High-Fidelity Medical Segmentation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-15T17:31:22.094714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6d38f7a-5ce3-4bdc-aa5b-3f50d9f7c17b · inbound

Specialization of softmax attention heads: insights from the high-dimensional single-location model cites this paper.

Specialization of softmax attention heads: insights from the high-dimensional single-location model Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T19:04:56.345504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7197e69a-8652-42d0-b412-28e3be129138 · inbound

ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training cites this paper.

ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-15T16:26:17.524856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e57a20e-424f-4706-9c6c-7195c75aeaa5 · inbound

SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation cites this paper.

SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-15T16:46:18.404563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af5ddb22-ff75-4f3e-88dd-f6c89e3c4c14 · inbound

SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation cites this paper.

SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T18:47:52.159297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40e735d8-be69-4b9a-8617-1fb1e8a789f9 · inbound

Stem: Rethinking Causal Information Flow in Sparse Attention cites this paper.

Stem: Rethinking Causal Information Flow in Sparse Attention Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T02:39:29.691195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a31cce4-041e-4968-933d-53e59a6cbdba · inbound

Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking cites this paper.

Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-25T06:55:26.156840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f1ce7c2-8d07-4dcd-9910-bad04b5284ec · inbound

UniICL: Systematizing Unified Multimodal In-context Learning through a Capability-Oriented Taxonomy cites this paper.

UniICL: Systematizing Unified Multimodal In-context Learning through a Capability-Oriented Taxonomy Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-13T18:42:33.178622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47ad2a28-4f53-4f61-b3c0-647ec8b500ac · inbound

AgenticRS-Architecture: System Design for Agentic Recommender Systems cites this paper.

AgenticRS-Architecture: System Design for Agentic Recommender Systems Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-14T23:23:15.920690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 925b5ea5-2929-40b4-988d-19b754f8e1e0 · inbound

Gradient Boosting within a Single Attention Layer cites this paper.

Gradient Boosting within a Single Attention Layer Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-13T20:28:14.154524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eaac5441-7830-45f0-8894-295516c9e21a · inbound

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows cites this paper.

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7634c9af-6064-430d-9ca6-4e05c69cc0d1 · inbound

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows cites this paper.

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-13T09:33:39.204257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecfb19e8-e101-4246-be3c-b7cad8f220d8 · inbound

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows cites this paper.

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-02T16:46:06.560385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa9cd05a-365e-46d3-b97a-39293e5f12d7 · inbound

Attention Editing: A Versatile Framework for Cross-Architecture Attention Conversion cites this paper.

Attention Editing: A Versatile Framework for Cross-Architecture Attention Conversion Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 42bbf5d8-f324-4500-a1cf-0e2f56946dff · inbound

GIANTS: Generative Insight Anticipation from Scientific Literature cites this paper.

GIANTS: Generative Insight Anticipation from Scientific Literature Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation edaa5544-700b-4f85-8d90-e8c020ac6ca3 · inbound

Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation cites this paper.

Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6d32629e-1f77-4dfb-8262-4f4eb2dd7217 · inbound

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds cites this paper.

TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0855b66-e95e-429e-8514-1aa397eeb832 · inbound

Attention to Mamba: A Recipe for Cross-Architecture Distillation cites this paper.

Attention to Mamba: A Recipe for Cross-Architecture Distillation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 27

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T23:08:25.056159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2083a784-fb0a-421b-84dc-5b45247c8a86 · inbound

LACE: Lattice Attention for Cross-thread Exploration cites this paper.

LACE: Lattice Attention for Cross-thread Exploration Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ccdf69dd-2962-4cb8-954f-70c0d52c5ebe · inbound

LACE: Lattice Attention for Cross-thread Exploration cites this paper.

LACE: Lattice Attention for Cross-thread Exploration Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab999bb8-8044-42d6-941a-3afddfac6692 · inbound

LACE: Lattice Attention for Cross-thread Exploration cites this paper.

LACE: Lattice Attention for Cross-thread Exploration Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a656e895-1788-4b47-97b7-be4ba313bb8d · inbound

SinkRouter: Sink-Aware Routing for Efficient Long-Context Decoding in Large Language and Multimodal Models cites this paper.

SinkRouter: Sink-Aware Routing for Efficient Long-Context Decoding in Large Language and Multimodal Models Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 84657d10-fb86-4fe1-9661-902a3c5b29a5 · inbound

Gated Memory Policy: In-Context Memorization and Adaptation cites this paper.

Gated Memory Policy: In-Context Memorization and Adaptation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad0c0a03-d13f-43cb-9882-c3ea2b790a1b · inbound

When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer cites this paper.

When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff78da4d-1d91-442d-8be2-c29a1a2cfe59 · inbound

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling cites this paper.

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f2dedcd-1993-46d8-a0e6-e3d9e727bad7 · inbound

Better Models, Faster Training: Sigmoid Attention for single-cell Foundation Models cites this paper.

Better Models, Faster Training: Sigmoid Attention for single-cell Foundation Models Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T09:46:27.682758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81f35d6c-e137-42eb-8d8c-b2c4c2ac24d1 · inbound

Heterogeneous Scientific Foundation Model Collaboration cites this paper.

Heterogeneous Scientific Foundation Model Collaboration Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 68127f9f-726e-4df4-a19e-25f462d4f683 · inbound

Let ViT Speak: Generative Language-Image Pre-training cites this paper.

Let ViT Speak: Generative Language-Image Pre-training Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5725efbf-e6a5-47cd-9845-ba7a22aca466 · inbound

Let ViT Speak: Generative Language-Image Pre-training cites this paper.

Let ViT Speak: Generative Language-Image Pre-training Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-01T07:35:28.744600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c9cd7ee6-010d-4ce0-8309-1eb79b91496f · inbound

Degradation-Aware Adaptive Context Gating for Unified Image Restoration cites this paper.

Degradation-Aware Adaptive Context Gating for Unified Image Restoration Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5148abd-78f5-4120-a522-871d1d5690f1 · inbound

A Cellular Doctrine of Morality: Intrinsic Active Precision and the Mind-Reality Overload Dilemma cites this paper.

A Cellular Doctrine of Morality: Intrinsic Active Precision and the Mind-Reality Overload Dilemma Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d5a33ea-5e1e-4e9a-84d4-f3b1f088ba0f · inbound

HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation cites this paper.

HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ed8e43f4-fde3-48a8-ae7a-0ebd7b667b63 · inbound

FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning cites this paper.

FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4e66e01-7836-4a00-9fcc-a87172dd1b2e · inbound

ZAYA1-8B Technical Report cites this paper.

ZAYA1-8B Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 204

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25137451-69cf-47e4-9448-59114c33f457 · inbound

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

Cubit: Token Mixer with Kernel Ridge Regression Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ec011d2a-6ed9-4e90-98af-c8e53f0e38ff · inbound

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

Cubit: Token Mixer with Kernel Ridge Regression Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-05-20T22:39:11.014913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4eccbe9-c28f-42da-89b8-91d679aa685d · inbound

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity cites this paper.

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 550c6bd5-0f61-4471-b454-e749db7bd390 · inbound

GEM: Generating LiDAR World Model via Deformable Mamba cites this paper.

GEM: Generating LiDAR World Model via Deformable Mamba Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31356f94-8b24-45a3-bfb6-3560282953c4 · inbound

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models cites this paper.

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e3029d2-b9df-457c-8b19-6e493226bef6 · inbound

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models cites this paper.

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-14T21:19:29.128327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7ff4173-f9ca-46ea-8457-33bac8f0992d · inbound

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 77d16551-9a87-4096-856c-9bce7c1d8a30 · inbound

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training cites this paper.

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-20T23:23:51.159285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54d58277-a2c2-4cba-90b5-e89ed8ef434c · inbound

RigidFormer: Learning Rigid Dynamics using Transformers cites this paper.

RigidFormer: Learning Rigid Dynamics using Transformers Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d17e455a-db99-41a9-b7ec-79264f63dcce · inbound

Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview cites this paper.

Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 132

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d3ae8cea-dd6f-4f25-abef-9c407274d267 · inbound

Mela: Test-Time Memory Consolidation based on Transformation Hypothesis cites this paper.

Mela: Test-Time Memory Consolidation based on Transformation Hypothesis Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c20a0ffa-543b-4e1d-8073-48ce5ba1d396 · inbound

HoloMotion-1 Technical Report cites this paper.

HoloMotion-1 Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-19T16:12:39.703943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 555c89b1-098c-4678-a7ce-5d80b21a217a · inbound

HoloMotion-1 Technical Report cites this paper.

HoloMotion-1 Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-20T20:33:43.157281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b220c595-3a3f-40f1-bbd7-287261d2effc · inbound

Registers Matter for Pixel-Space Diffusion Transformers cites this paper.

Registers Matter for Pixel-Space Diffusion Transformers Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-20T19:08:54.039719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb6591b3-dbbc-4fb1-9c4d-787185775c49 · inbound

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor cites this paper.

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-21T06:19:42.056549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5f3f0ea-8ace-4d80-a8b3-5662a8a41f27 · inbound

HorizonStream: Long-Horizon Attention for Streaming 3D Reconstruction cites this paper.

HorizonStream: Long-Horizon Attention for Streaming 3D Reconstruction Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-25T04:35:20.830525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b8790965-308c-4768-9fab-c3de4398cba1 · inbound

Inference Time Optimization with Confidence Dynamics cites this paper.

Inference Time Optimization with Confidence Dynamics Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-06-30T11:14:37.619553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2bbb6b89-42b9-4e28-801e-b39bf8472521 · inbound

MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing cites this paper.

MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-06-29T12:43:26.137383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd8b8d80-e78d-44ea-9af6-6eaa1b4f3881 · inbound

Meta-Attention: Bayesian Per-Token Routing for Efficient Transformer Inference cites this paper.

Meta-Attention: Bayesian Per-Token Routing for Efficient Transformer Inference Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-06-29T14:53:31.426670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c88a9ab-a526-48cd-97fb-b2017a19223a · inbound

Dynamics of Stochastic Momentum with Sparse Updates in High Dimensions cites this paper.

Dynamics of Stochastic Momentum with Sparse Updates in High Dimensions Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T10:03:17.598092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 082140ca-8cd1-4090-9133-1dfc69cc8858 · inbound

NuGNN: a Graph Neural Network for Nuclear Reaction Network Equations cites this paper.

NuGNN: a Graph Neural Network for Nuclear Reaction Network Equations Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 30

Resolution: metadata mismatch; local_arxiv, observed 2026-06-28T04:31:39.363353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 12ca6e68-bd80-4410-bbad-8227896f7975 · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-06-28T23:32:46.579273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 304325bb-2013-4930-9647-402ea341c43b · inbound

Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating cites this paper.

Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 28

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T00:47:29.992991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cb2fa227-33cc-498a-a96d-b5951d68e200 · inbound

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction cites this paper.

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-06-27T14:00:59.321902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3e7cf88-a1da-4a62-b7c6-1dbb1a765283 · inbound

Enhancing Multilingual Reasoning via Steerable Model Merging cites this paper.

Enhancing Multilingual Reasoning via Steerable Model Merging Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-06-26T20:39:56.013606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4977a195-6311-4017-b7dc-5f5f1c99f7fb · inbound

Physics-Informed Neural Network with Squeeze-Excitation-like Attention cites this paper.

Physics-Informed Neural Network with Squeeze-Excitation-like Attention Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:29:30.221194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a21c9084-b358-4c53-9610-8a1e1a6bea9a · inbound

QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging cites this paper.

QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:09:29.299310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b95b062-f817-4f23-9d07-7af82b3be829 · inbound

Tapered Language Models cites this paper.

Tapered Language Models Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-04T10:09:44.028492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05fa2581-f02b-4f39-8813-3504db4ec9a7 · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 245

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T18:40:03.189541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05f332bf-385b-4106-86bd-faeef8ebd2e8 · inbound

ZONOS2 Technical Report cites this paper.

ZONOS2 Technical Report Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 245

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T18:15:59.083536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e70b8b98-3b18-46b3-bb8e-d7a1616cb757 · inbound

Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control cites this paper.

Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-04T17:09:59.580966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 68396d77-711d-4937-b619-c85cc6908f2f · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-04T20:50:11.376633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3db5491e-cbb9-40b3-8ba1-7f328006fd93 · inbound

WQ-Fusion: Dynamic Gated Attention for Cross-Domain Audio Representation cites this paper.

WQ-Fusion: Dynamic Gated Attention for Cross-Domain Audio Representation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-04T14:39:57.192372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0c0e05f-a3fd-4078-b302-d0db223c6e4d · inbound

Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet cites this paper.

Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-07-01T12:05:42.850070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a4e7380-566f-4dce-885f-15d95957612a · inbound

Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG cites this paper.

Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-12T05:47:14.970921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6a1b239-3378-42fa-b8f6-fc9d1b29f4be · inbound

Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity cites this paper.

Sparse Delta Memory: Scaling the State of Linear RNNs through Sparsity Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 105

Resolution: verified exact; local_arxiv, observed 2026-07-09T12:46:14.643488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 651f180f-6528-4db2-9654-47a0eeded357 · inbound

Higher-Order Cell Tracking Transformer cites this paper.

Higher-Order Cell Tracking Transformer Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-14T03:25:28.859078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bfcc3a3-82c9-46ae-a547-06add249eac1 · inbound

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data cites this paper.

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T09:50:21.593887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07381105-25cd-4c2f-92b9-32f906cf5671 · inbound

A Controlled Study of Attention-Only Transformers cites this paper.

A Controlled Study of Attention-Only Transformers Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T16:19:08.214613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cced871c-cbd8-48f4-bf59-aee4cac671e4 · inbound

SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation cites this paper.

SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T07:09:07.598100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b9e3460-be13-44e7-8036-003aff7128e8 · inbound

Multi-Head Attention Residuals cites this paper.

Multi-Head Attention Residuals Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T11:39:23.239954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 066941b7-06c5-4da0-b5ea-677bcaf10f87 · inbound

Multi-Head Attention Residuals cites this paper.

Multi-Head Attention Residuals Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T01:39:48.142895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d7637e3-367a-449d-b3d9-dd58750cd97e · inbound

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution cites this paper.

ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 214

Resolution: unresolved; no resolver link, observed 2026-08-01T09:52:09.594240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.