Elucidating the Design Space of Decay in Linear Attention | Paper Citation Record

Observation 89891472-2fa5-44e8-925f-cfb8caae3765 · outbound

This paper cites Simple linear attention language models balance the recall-throughput tradeoff.

Elucidating the Design Space of Decay in Linear Attention Simple linear attention language models balance the recall-throughput tradeoff

Reference 1

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Observation fbda726b-c63e-4033-88d7-3414895c8eab · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Elucidating the Design Space of Decay in Linear Attention xLSTM: Extended Long Short-Term Memory

Reference 2

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Observation 1888d2b2-25dd-42c7-9a41-56c1d7a27c0a · outbound

This paper cites Rethinking attention with performers.

Elucidating the Design Space of Decay in Linear Attention Rethinking attention with performers

Reference 3

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Observation 8ce69a5d-0c37-4b7c-844e-dd545dae5051 · outbound

This paper cites Meta LA : Unified optimal linear approximation to softmax attention map.

Elucidating the Design Space of Decay in Linear Attention Meta LA : Unified optimal linear approximation to softmax attention map

Reference 4

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Observation 17ddef9c-8d2e-4d37-bb13-6aaa2b259756 · outbound

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

Elucidating the Design Space of Decay in Linear Attention Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 5

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

Elucidating the Design Space of Decay in Linear Attention Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 6

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This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

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

Reference 7

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Observation 80a6e3bf-7154-4573-8aa2-2a7e5c7a6158 · outbound

This paper cites A framework for few-shot language model evaluation.

Elucidating the Design Space of Decay in Linear Attention A framework for few-shot language model evaluation

Reference 8

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Observation 9020ca47-65f3-44c3-ae7b-b1ce26fa2f3c · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

Elucidating the Design Space of Decay in Linear Attention Mamba: Linear-time sequence modeling with selective state spaces

Reference 9

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Observation 74f7fc5d-25a4-4afc-ac7f-e7c6cb9a87b6 · outbound

This paper cites HiPPO: Recurrent Memory with Optimal Polynomial Projections.

Elucidating the Design Space of Decay in Linear Attention HiPPO: Recurrent Memory with Optimal Polynomial Projections

Reference 10

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Observation 988da5e4-92ea-4f2d-b320-f4bb1aabb9e4 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Elucidating the Design Space of Decay in Linear Attention Efficiently modeling long sequences with structured state spaces

Reference 11

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This paper cites Efficiently modeling long sequences with structured state spaces, 2022 b.

Elucidating the Design Space of Decay in Linear Attention Efficiently modeling long sequences with structured state spaces, 2022 b

Reference 12

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Observation e75809a8-0695-4c3d-97ef-236cdbde74f2 · outbound

This paper cites How to train your HIPPO : State space models with generalized orthogonal basis projections.

Elucidating the Design Space of Decay in Linear Attention How to train your HIPPO : State space models with generalized orthogonal basis projections

Reference 13

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Observation 614405b5-0b03-47cb-8a96-d4f6a41b038f · outbound

This paper cites Diagonal state spaces are as effective as structured state spaces.

Elucidating the Design Space of Decay in Linear Attention Diagonal state spaces are as effective as structured state spaces

Reference 14

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This paper cites Long short-term memory.

Elucidating the Design Space of Decay in Linear Attention Long short-term memory

Reference 15

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This paper cites Mini CPM : Unveiling the potential of small language models with scalable training strategies.

Elucidating the Design Space of Decay in Linear Attention Mini CPM : Unveiling the potential of small language models with scalable training strategies

Reference 16

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Observation 8187a2b6-1e3d-4f66-ba68-3761845ed41a · outbound

This paper cites karpathy/char-rnn.

Elucidating the Design Space of Decay in Linear Attention karpathy/char-rnn

Reference 17

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Observation 8c6d6b96-a4d1-4091-b702-27f6a0212af8 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Elucidating the Design Space of Decay in Linear Attention Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 18

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Observation f04b8b1f-c3bf-478c-a523-8c43b076e9f1 · outbound

This paper cites Decoupled weight decay regularization.

Elucidating the Design Space of Decay in Linear Attention Decoupled weight decay regularization

Reference 19

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Observation cb6d689f-c2b5-4df3-acf8-86b65904c0a7 · outbound

This paper cites Mega: Moving Average Equipped Gated Attention.

Elucidating the Design Space of Decay in Linear Attention Mega: Moving Average Equipped Gated Attention

Reference 20

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Observation de140dd9-4a9e-4514-b005-d20e03d6f1b1 · outbound

This paper cites Megalodon: Efficient LLM pretraining and inference with unlimited context length.

Elucidating the Design Space of Decay in Linear Attention Megalodon: Efficient LLM pretraining and inference with unlimited context length

Reference 21

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Observation 566b7909-647f-4d16-abbe-2aec11a9c699 · outbound

This paper cites Parallelizing linear recurrent neural nets over sequence length.

Elucidating the Design Space of Decay in Linear Attention Parallelizing linear recurrent neural nets over sequence length

Reference 22

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This paper cites Smith, Albert Gu, Anushan Fernando, C aglar G \" u l c ehre, Razvan Pascanu, and Soham De.

Elucidating the Design Space of Decay in Linear Attention Smith, Albert Gu, Anushan Fernando, C aglar G \" u l c ehre, Razvan Pascanu, and Soham De

Reference 23

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Observation fc08e730-ffc7-4d7d-a912-79fe79728fa0 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Elucidating the Design Space of Decay in Linear Attention Pytorch: An imperative style, high-performance deep learning library

Reference 24

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This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.

Elucidating the Design Space of Decay in Linear Attention The fineweb datasets: Decanting the web for the finest text data at scale

Reference 25

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Observation 7607eb1f-61b0-4137-92c7-92124f863628 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Elucidating the Design Space of Decay in Linear Attention RWKV: Reinventing RNNs for the Transformer Era

Reference 26

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This paper cites Rwkv: Reinventing rnns for the transformer era.

Elucidating the Design Space of Decay in Linear Attention Rwkv: Reinventing rnns for the transformer era

Reference 27

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Observation 14ef27a6-2ae2-48c8-b59c-aee978d5354d · outbound

This paper cites Eagle and finch: RWKV with matrix-valued states and dynamic recurrence.

Elucidating the Design Space of Decay in Linear Attention Eagle and finch: RWKV with matrix-valued states and dynamic recurrence

Reference 28

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Observation c23e5586-a6fe-487a-b574-1b9abe48f768 · outbound

This paper cites Wind, Tianyi Wu, Daniel Wuttke, and Christian Zhou-Zheng.

Elucidating the Design Space of Decay in Linear Attention Wind, Tianyi Wu, Daniel Wuttke, and Christian Zhou-Zheng

Reference 29

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Observation d30f7494-0234-4161-bc89-1c7483ff83a8 · outbound

This paper cites Random Feature Attention.

Elucidating the Design Space of Decay in Linear Attention Random Feature Attention

Reference 30

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This paper cites Xmixers: A collection of SOTA efficient token/channel mixers , August 2025.

Elucidating the Design Space of Decay in Linear Attention Xmixers: A collection of SOTA efficient token/channel mixers , August 2025

Reference 31

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This paper cites cosformer: Rethinking softmax in attention.

Elucidating the Design Space of Decay in Linear Attention cosformer: Rethinking softmax in attention

Reference 32

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This paper cites The Devil in Linear Transformer.

Elucidating the Design Space of Decay in Linear Attention The Devil in Linear Transformer

Reference 33

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This paper cites Toeplitz neural network for sequence modeling.

Elucidating the Design Space of Decay in Linear Attention Toeplitz neural network for sequence modeling

Reference 34

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Observation 219a30f7-8592-4f92-b4d1-3df23f1fa5c5 · outbound

This paper cites TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer.

Elucidating the Design Space of Decay in Linear Attention TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer

Reference 35

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This paper cites Linearized relative positional encoding.

Elucidating the Design Space of Decay in Linear Attention Linearized relative positional encoding

Reference 36

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This paper cites Hierarchically gated recurrent neural network for sequence modeling.

Elucidating the Design Space of Decay in Linear Attention Hierarchically gated recurrent neural network for sequence modeling

Reference 37

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This paper cites Various lengths, constant speed: Efficient language modeling with lightning attention.

Elucidating the Design Space of Decay in Linear Attention Various lengths, constant speed: Efficient language modeling with lightning attention

Reference 38

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This paper cites HGRN2: Gated Linear RNNs with State Expansion.

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

Reference 39

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This paper cites You only scan once: Efficient multi-dimension sequential modeling with lightnet, 2025.

Elucidating the Design Space of Decay in Linear Attention You only scan once: Efficient multi-dimension sequential modeling with lightnet, 2025

Reference 40

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This paper cites Language models are unsupervised multitask learners.

Elucidating the Design Space of Decay in Linear Attention Language models are unsupervised multitask learners

Reference 41

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Elucidating the Design Space of Decay in Linear Attention GLU Variants Improve Transformer

Reference 42

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Elucidating the Design Space of Decay in Linear Attention Unresolved cited work

Reference 43

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

Elucidating the Design Space of Decay in Linear Attention RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 44

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

Elucidating the Design Space of Decay in Linear Attention Retentive Network: A Successor to Transformer for Large Language Models

Reference 45

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This paper cites FLA: A Triton-Based Library for Hardware-Efficient Implementations of Linear Attention Mechanism , January 2024 a.

Elucidating the Design Space of Decay in Linear Attention FLA: A Triton-Based Library for Hardware-Efficient Implementations of Linear Attention Mechanism , January 2024 a

Reference 46

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Elucidating the Design Space of Decay in Linear Attention Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism, January 2024 b

Reference 47

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

Elucidating the Design Space of Decay in Linear Attention Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 48

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This paper cites Parallelizing Linear Transformers with the Delta Rule over Sequence Length.

Elucidating the Design Space of Decay in Linear Attention Parallelizing Linear Transformers with the Delta Rule over Sequence Length

Reference 49

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Elucidating the Design Space of Decay in Linear Attention Gated delta networks: Improving mamba2 with delta rule

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:22.554528Z

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This paper cites Gated slot attention for efficient linear-time sequence modeling.

Elucidating the Design Space of Decay in Linear Attention Gated slot attention for efficient linear-time sequence modeling

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:22.522156Z

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Elucidating the Design Space of Decay in Linear Attention fla-org/flame

Reference 52

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Observation b3281d0e-2cc7-4aa6-87e4-6edd7db4945d · outbound

This paper cites write newline.

Elucidating the Design Space of Decay in Linear Attention write newline

Reference 53

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Observation f3565951-46e0-4f4b-899e-37a0efc29770 · outbound

This paper cites @esa (Ref.

Elucidating the Design Space of Decay in Linear Attention @esa (Ref

Reference 54

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Elucidating the Design Space of Decay in Linear Attention Unresolved cited work

Reference 55

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Elucidating the Design Space of Decay in Linear Attention Unresolved cited work

Reference 56

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