Paper Citation Record · LEDGER

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 46 inbound Pith citation observations for arXiv:2006.16236.

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

pith.paper-citation-record.v1
2006.16236 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 46 of 46 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:44:48.233553Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T12:46:14.661055Z

Reference resolution

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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 74099416-bc4c-4426-9e04-32e38ec29945 · inbound

Rethinking Attention with Performers cites this paper.

Rethinking Attention with Performers Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 132

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:16:14.479854Z

Source-reported events for the cited work

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Observation 82f2ab52-4f6a-4bb0-a2c0-0c48c08e9616 · inbound

Deformable DETR: Deformable Transformers for End-to-End Object Detection cites this paper.

Deformable DETR: Deformable Transformers for End-to-End Object Detection Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:47:16.992761Z

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Observation 452e639c-9a53-41c3-9be9-ab3616972d59 · inbound

Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization cites this paper.

Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 7

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Observation 690e44ed-757d-411e-868e-ae61c42ee29c · inbound

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization cites this paper.

A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 30

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Observation bdde8bfd-24b9-4bf4-a1df-1c67f13f95ed · inbound

Workflow-Based Evaluation of Music Generation Systems cites this paper.

Workflow-Based Evaluation of Music Generation Systems Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:03.218234Z

Source-reported events for the cited work

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Observation 0f3f5402-67c2-40bd-8261-d2e154e12313 · inbound

Scaling Context Requires Rethinking Attention cites this paper.

Scaling Context Requires Rethinking Attention Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:00:43.755014Z

Source-reported events for the cited work

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Observation 3bb37966-500c-4234-9637-c71ec7576f63 · inbound

Evaluation of Finetuned LLMs in AMR Parsing cites this paper.

Evaluation of Finetuned LLMs in AMR Parsing Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 32

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Observation 86678972-059a-4d82-8ab9-b98aec955c35 · inbound

Rethinking Transformer Connectivity: TLinFormer, A Path to Exact, Full Context-Aware Linear Attention cites this paper.

Rethinking Transformer Connectivity: TLinFormer, A Path to Exact, Full Context-Aware Linear Attention Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 5

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Observation a73fd013-e27c-48c0-b678-fbb90b355fa2 · inbound

WaveLLDM: Design and Development of a Lightweight Latent Diffusion Model for Speech Enhancement and Restoration cites this paper.

WaveLLDM: Design and Development of a Lightweight Latent Diffusion Model for Speech Enhancement and Restoration Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 18

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Observation 1fc59587-a1bb-4feb-83d2-473b16bbe591 · inbound

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:01:46.263295Z

Source-reported events for the cited work

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Observation e4c0e263-a0fa-4f17-bb7c-73acf485a3d0 · inbound

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.

Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T10:25:17.118921Z

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Observation dc923eb5-9062-4833-a75c-873c904a5a86 · inbound

Customizing the Inductive Biases of Softmax Attention using Structured Matrices cites this paper.

Customizing the Inductive Biases of Softmax Attention using Structured Matrices Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 28

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Observation 75d6ce3f-96e3-4551-be7f-a37e1bbe9aec · inbound

ICR-RL: Deep Reinforcement Learning via In-Context Regression cites this paper.

ICR-RL: Deep Reinforcement Learning via In-Context Regression Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 12

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Observation c1be79be-413b-4704-80ab-10ba0ff07d92 · inbound

StateX: Enhancing RNN Recall via Post-training State Expansion cites this paper.

StateX: Enhancing RNN Recall via Post-training State Expansion Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T12:31:22.164668Z

Source-reported events for the cited work

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Observation 89ba6797-f20c-4158-8323-acfd73e12373 · inbound

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

Short window attention enables long-term memorization Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:11:21.778307Z

Source-reported events for the cited work

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Observation fe1ba79f-ba64-42de-9f3d-ec744f5954ce · inbound

A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents cites this paper.

A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 157

Resolution: unresolved; no resolver link, observed 2026-08-04T08:12:25.317841Z

Source-reported events for the cited work

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Observation 98d884bb-8b89-4086-8f2f-2462390adace · inbound

NVIDIA Nemotron 3: Efficient and Open Intelligence cites this paper.

NVIDIA Nemotron 3: Efficient and Open Intelligence Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 112

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T01:40:42.558358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 372e6283-b3cc-434a-99f4-efe62bdcfd4d · inbound

Cognitive Field Theory: Memory-Dressed Collective Dynamics of Intelligence cites this paper.

Cognitive Field Theory: Memory-Dressed Collective Dynamics of Intelligence Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T10:26:02.691384Z

Source-reported events for the cited work

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Observation f614d458-3e98-496e-be91-63003e45d623 · inbound

Cognitive Field Theory: Memory-Dressed Collective Dynamics of Intelligence cites this paper.

Cognitive Field Theory: Memory-Dressed Collective Dynamics of Intelligence Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T06:26:45.132747Z

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Observation feb237d2-3086-40df-b318-b6efd50a617a · inbound

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems cites this paper.

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:47:53.817204Z

Source-reported events for the cited work

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Observation 5224929e-12c7-44a6-9940-3a857b103342 · inbound

Incremental Transformer Neural Processes cites this paper.

Incremental Transformer Neural Processes Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-02T21:52:58.267170Z

Source-reported events for the cited work

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Observation 0ad0c30e-27f8-46fa-9004-c7da7b492dcf · inbound

In-Place Test-Time Training cites this paper.

In-Place Test-Time Training Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:49.133097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 95506fcd-ebda-4eca-8e53-877137fdf0f2 · 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 Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:16:09.990965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44e7b85b-b778-4144-80a9-53ec2be4fbc4 · inbound

StreamIndex: Memory-Bounded Compressed Sparse Attention via Streaming Top-k cites this paper.

StreamIndex: Memory-Bounded Compressed Sparse Attention via Streaming Top-k Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:15:37.711744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c1486958-4903-46f8-8d34-7679d9c5caf1 · inbound

Retrieval from Within: An Intrinsic Capability of Attention-Based Models cites this paper.

Retrieval from Within: An Intrinsic Capability of Attention-Based Models Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:41:10.635190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2d816f37-efa7-404c-a93f-0281000c4b3c · inbound

Retrieval from Within: An Intrinsic Capability of Attention-Based Models cites this paper.

Retrieval from Within: An Intrinsic Capability of Attention-Based Models Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:50:55.309782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1041b59f-9f5b-40b3-8c6a-165793753671 · inbound

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models cites this paper.

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:56.466340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b0ad838b-8623-4a0d-afd0-dde5e28996e3 · inbound

Structured Recurrent Mixers for Massively Parallelized Sequence Generation cites this paper.

Structured Recurrent Mixers for Massively Parallelized Sequence Generation Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:29.292952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c74920c1-f39e-426f-84ce-902cbfd4bd0d · inbound

Structured Recurrent Mixers for Massively Parallelized Sequence Generation cites this paper.

Structured Recurrent Mixers for Massively Parallelized Sequence Generation Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:29:12.972639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a2934cc9-9055-475b-b918-c756437d2c85 · inbound

Structured Recurrent Mixers for Massively Parallelized Sequence Generation cites this paper.

Structured Recurrent Mixers for Massively Parallelized Sequence Generation Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:35:07.328572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 49206b55-4541-4c99-a12f-9a4f69f3c6e4 · inbound

Structured Recurrent Mixers for Massively Parallelized Sequence Generation cites this paper.

Structured Recurrent Mixers for Massively Parallelized Sequence Generation Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 13

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Observation 979b1979-1bfe-4872-bbdd-0712130f9f03 · inbound

The Transformer as a Polar State Estimator cites this paper.

The Transformer as a Polar State Estimator Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:17:07.442265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0ff97c4-7f37-4d57-837f-98a0fd7ae4ff · inbound

WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:59:28.589590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8f212a8b-c846-440c-b1f9-1774a1ca34ee · inbound

WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:59:45.116299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6d9076f4-5a6d-40b3-a227-784424cee5be · inbound

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction cites this paper.

Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:57:53.690756Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 41117b28-8a1a-4c7f-a2f2-edc4a13faa1d · inbound

Gated Bidirectional Linear Attention for Generative Retrieval cites this paper.

Gated Bidirectional Linear Attention for Generative Retrieval Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:17:22.286657Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 456d36a7-be50-4ce9-ab82-4597bfabc4cf · inbound

Q-Delta: Beyond Key-Value Associative State Evolution cites this paper.

Q-Delta: Beyond Key-Value Associative State Evolution Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.570441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0788782e-4c80-46fd-83ab-6b9d27bf5e06 · inbound

Memory-Managed Long-Context Attention: Bounded Editable Memory with a Hard Lifecycle and Calibrated Sparse Fallback cites this paper.

Memory-Managed Long-Context Attention: Bounded Editable Memory with a Hard Lifecycle and Calibrated Sparse Fallback Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:54:35.230604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19276440-1143-4da1-8f1b-413239946bf5 · inbound

Memory-Managed Long-Context Attention: Bounded Editable Memory with a Hard Lifecycle and Calibrated Sparse Fallback cites this paper.

Memory-Managed Long-Context Attention: Bounded Editable Memory with a Hard Lifecycle and Calibrated Sparse Fallback Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 1

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Source-reported events for the cited work

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Observation 2a52627a-b295-4603-943a-ad2e1b5adc5d · inbound

A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets cites this paper.

A Hippocampus for Linear Attention: An Exact Memory for What the Recurrent State Forgets Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:48:20.302560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 232e1d56-7592-4206-a07f-1c052c404ee7 · inbound

ELiTeFormer: An Efficient Transformer for FPGAs cites this paper.

ELiTeFormer: An Efficient Transformer for FPGAs Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T00:55:09.690245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5254e790-91d8-4e3b-af19-0a9d309e51ec · 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 Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 96

Resolution: verified exact; local_arxiv, observed 2026-07-09T12:46:14.662427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ef665924-8d71-4229-b00b-aa9f48e8eea5 · inbound

Infrared Organization and Critical Cognitive Field Formation in Transformer Dynamics cites this paper.

Infrared Organization and Critical Cognitive Field Formation in Transformer Dynamics Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T08:17:20.497119Z

Source-reported events for the cited work

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Observation 7146ade8-5bd8-49fc-8d4d-e7539b2db5ec · inbound

Infrared Organization and Critical Cognitive Field Formation in Transformer Dynamics cites this paper.

Infrared Organization and Critical Cognitive Field Formation in Transformer Dynamics Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T07:10:13.996414Z

Source-reported events for the cited work

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Observation a313df4e-2275-461a-ae53-5a001b719130 · inbound

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States cites this paper.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T15:02:08.396594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8bafb3e-05a2-4491-8723-5253ca427a75 · inbound

GLIDE: Guided Layerwise Hybrid Attention for Efficient LLM Inference cites this paper.

GLIDE: Guided Layerwise Hybrid Attention for Efficient LLM Inference Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 5165

Resolution: unresolved; no resolver link, observed 2026-08-02T09:52:43.533456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.