Paper Citation Record · LEDGER

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform

As of 20 August 2026, this Paper Citation Record lists 100 of 214 outbound references and 3 inbound Pith citation observations for arXiv:2505.11892.

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

pith.paper-citation-record.v1
2505.11892 v1

Coverage vector

measured 100 of 214 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:56:34.179657Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

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

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:10:57.183708Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:36:04.771630Z

Reference resolution

100 of 214 outbound references displayed

verified exact0
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External citation measurements

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Outbound references

Observation 7e07b8cc-7d0e-4ccc-9a72-1d3826f47112 · outbound

This paper cites Optimal-degree polynomial approximations for exponentials and gaussian kernel density estimation.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Optimal-degree polynomial approximations for exponentials and gaussian kernel density estimation

Reference 1

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Observation 7be883c7-b37f-4acd-95a5-8042c7e3de85 · outbound

This paper cites GPT-4 Technical Report.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform GPT-4 Technical Report

Reference 2

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Observation ef4f2f7e-261c-4c9d-9cdc-560bc313ec07 · outbound

This paper cites Computational complexity: a modern approach.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Computational complexity: a modern approach

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.760572Z

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Observation 2292691b-6a06-49a8-af3e-95cec780ad9c · outbound

This paper cites A fast algorithm for aperiodic linear stencil computation using fast fourier transforms.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform A fast algorithm for aperiodic linear stencil computation using fast fourier transforms

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.765517Z

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Observation d082cdc4-56bc-4040-9edd-9894d1484fdc · outbound

This paper cites Linear attention is (maybe) all you need (to understand transformer optimization).

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Linear attention is (maybe) all you need (to understand transformer optimization)

Reference 5

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Observation da0114c3-4b03-4c53-926c-78f6fa2bf6ef · outbound

This paper cites A few remarks on the operator norm of random toeplitz matrices.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform A few remarks on the operator norm of random toeplitz matrices

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.774745Z

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Observation 4a631dc1-84ec-49b5-862d-a82450cbfa48 · outbound

This paper cites Introducing meta llama 3: The most capable openly available llm to date, 2024.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Introducing meta llama 3: The most capable openly available llm to date, 2024

Reference 7

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Observation ca339a01-9b73-4965-9255-66e9b744cfc3 · outbound

This paper cites Foundation models defining a new era in vision: a survey and outlook.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Foundation models defining a new era in vision: a survey and outlook

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.785200Z

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Observation 36582258-875f-4101-a186-ae0f85a899f7 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku, 2024.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform The claude 3 model family: Opus, sonnet, haiku, 2024

Reference 9

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Observation e17b51ca-6c65-4d74-977f-67bdbb91eae9 · outbound

This paper cites Faster walsh-hadamard and discrete fourier transforms from matrix non-rigidity.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Faster walsh-hadamard and discrete fourier transforms from matrix non-rigidity

Reference 10

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Observation d424b01b-cad9-4452-bedb-01779f016e7b · outbound

This paper cites Fast attention requires bounded entries.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Fast attention requires bounded entries

Reference 11

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Observation 8188c51c-2729-4232-9f31-873e3b0d7f98 · outbound

This paper cites The fine-grained complexity of gradient computation for training large language models.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform The fine-grained complexity of gradient computation for training large language models

Reference 12

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Observation b52d3650-e2fb-4c92-862d-f3933cbc08a7 · outbound

This paper cites How to capture higher-order correlations? generalizing matrix softmax attention to kronecker computation.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform How to capture higher-order correlations? generalizing matrix softmax attention to kronecker computation

Reference 13

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Observation 8212c565-e211-4b6e-8f58-4574cd4494c3 · outbound

This paper cites Only large weights (and not skip connections) can prevent the perils of rank collapse.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Only large weights (and not skip connections) can prevent the perils of rank collapse

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.809000Z

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Observation 1e14fcd3-09c4-43cf-9b46-2b10c83127cb · outbound

This paper cites Normalized iterative hard thresholding: Guaranteed stability and performance.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Normalized iterative hard thresholding: Guaranteed stability and performance

Reference 15

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Observation b2a07199-93e1-4f84-aa54-48250f10c2d3 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 16

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Observation 1dc47fdd-87f9-410f-84a6-57defb67d8b9 · outbound

This paper cites On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , pages 610--623, 2021.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , pages 610--623, 2021

Reference 17

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Observation 79b1df9d-20d5-4523-bbd9-0ad553df2e04 · outbound

This paper cites Exploring Alternatives to Softmax Function.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Exploring Alternatives to Softmax Function

Reference 18

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Observation 077cf75a-88fb-4869-9723-948376c61f61 · outbound

This paper cites Language models are few-shot learners.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Language models are few-shot learners

Reference 19

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Observation 2929ce32-54b1-4404-b268-519506a66c7b · outbound

This paper cites An improved estimate in the restricted isometry problem.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform An improved estimate in the restricted isometry problem

Reference 20

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Observation bc8bb7e8-0e20-4b06-933e-50c1e309053d · outbound

This paper cites Longformer: The Long-Document Transformer.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Longformer: The Long-Document Transformer

Reference 21

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Observation ffb583a6-7125-4aba-89a6-f45ade5a483b · outbound

This paper cites Training (overparametrized) neural networks in near-linear time.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Training (overparametrized) neural networks in near-linear time

Reference 22

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Observation ffc4df77-35ad-4074-852d-bbc43e9ddfb5 · outbound

This paper cites Convex optimization: Algorithms and complexity.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Convex optimization: Algorithms and complexity

Reference 23

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Observation 12ed17b6-e0b2-4419-ac65-927930d46ba7 · outbound

This paper cites Crawling facebook for social network analysis purposes.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Crawling facebook for social network analysis purposes

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.851153Z

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Observation 69144943-d0d6-4943-ac1d-0af11df1a0e7 · outbound

This paper cites High-Order Matching for One-Step Shortcut Diffusion Models.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform High-Order Matching for One-Step Shortcut Diffusion Models

Reference 25

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Observation 25897ce5-c50a-4db5-b125-358a4fb23b0f · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Generating Long Sequences with Sparse Transformers

Reference 26

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Observation 961a0d47-0d4e-4f57-b355-f26f0f4869d1 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Scaling Instruction-Finetuned Language Models

Reference 27

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Observation 6992f4e3-809d-45ef-b228-cdff2fb838dd · outbound

This paper cites Dihan: A novel dynamic hierarchical graph attention network for fake news detection.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Dihan: A novel dynamic hierarchical graph attention network for fake news detection

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.869877Z

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Observation cf978dfa-c7f2-41a9-947f-848781cd68a0 · outbound

This paper cites Fast gradient computation for rope attention in almost linear time.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Fast gradient computation for rope attention in almost linear time

Reference 29

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Observation 3783597a-025d-47b5-b9fa-ecff6a9df8ad · outbound

This paper cites Kernel density estimation through density constrained near neighbor search.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Kernel density estimation through density constrained near neighbor search

Reference 30

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Observation b10d37c9-e55c-4992-be8c-a378ef342a4d · outbound

This paper cites Circuit Complexity Bounds for RoPE-based Transformer Architecture.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Circuit Complexity Bounds for RoPE-based Transformer Architecture

Reference 31

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Observation 4aeb2a27-4b93-4006-98c9-6ff7c2d880be · outbound

This paper cites HSR-Enhanced Sparse Attention Acceleration.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform HSR-Enhanced Sparse Attention Acceleration

Reference 32

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Observation e50143fb-9224-4462-9a74-63267807f670 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform PaLM: Scaling Language Modeling with Pathways

Reference 33

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Observation 9aa7adf5-7617-428f-aa69-87446aeb69a2 · outbound

This paper cites Active regression via linear-sample sparsification.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Active regression via linear-sample sparsification

Reference 34

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Observation 32a23cf7-42ab-477d-8674-15b5e116dc97 · outbound

This paper cites Estimating the frequency of a clustered signal.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Estimating the frequency of a clustered signal

Reference 35

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Observation c9fe98bc-bae7-4489-8084-08041834bcc1 · outbound

This paper cites Ld-pruner: Efficient pruning of latent diffusion models using task-agnostic insights.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Ld-pruner: Efficient pruning of latent diffusion models using task-agnostic insights

Reference 36

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Observation ab4b0da9-791e-45aa-9f82-750b19a9090e · outbound

This paper cites Bronstein, and Max Hansmire.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Bronstein, and Max Hansmire

Reference 37

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Observation 2e071d70-a722-4db6-adac-40214bb3392b · outbound

This paper cites An algorithm for the machine calculation of complex fourier series.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform An algorithm for the machine calculation of complex fourier series

Reference 38

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Observation f9936814-1c0b-414e-8777-0e88f8de773c · outbound

This paper cites Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE transactions on information theory , 52(12):5406--5425, 2006.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE transactions on information theory , 52(12):5406--5425, 2006

Reference 39

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Observation e62fc2a3-deb1-40a5-a21c-37faee742930 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 40

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Observation 55e4ea5a-8770-4f81-9859-1fb53bdeb58b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 41

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Observation 8c05b76d-7430-40d2-9228-70bd05fa8a62 · outbound

This paper cites BERT : Pre-training of deep bidirectional transformers for language understanding.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 42

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This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 43

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Observation 32ef5d16-8270-4c66-8b48-f4d75a7c10ec · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Attentive walk-aggregating graph neural networks

Reference 44

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Observation 292b0d0c-123e-4c64-8557-c5160a9def55 · outbound

This paper cites Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Randomized and Deterministic Attention Sparsification Algorithms for Over-parameterized Feature Dimension

Reference 45

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Observation f96031d2-ee27-4f3d-8053-5a8d456f7cbb · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Swiftbrush v2: Make your one-step diffusion model better than its teacher

Reference 46

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Sketch-gnn: Scalable graph neural networks with sublinear training complexity

Reference 47

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Observation 84ec62be-2acc-48db-b123-2de1aaf71eae · outbound

This paper cites Streaming Kernel PCA Algorithm With Small Space.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Streaming Kernel PCA Algorithm With Small Space

Reference 48

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Superiority of softmax: Unveiling the performance edge over linear attention

Reference 49

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Observation b2af403f-75c6-4fa7-8d0c-1340ff336cf7 · outbound

This paper cites Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting

Reference 50

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This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 51

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Observation b59621c3-8f7b-4723-af15-85440f8e9b0f · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform One Step Diffusion via Shortcut Models

Reference 52

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This paper cites Sagn: semantic adaptive graph network for skeleton-based human action recognition.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Sagn: semantic adaptive graph network for skeleton-based human action recognition

Reference 53

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Observation 9436b2de-d852-4557-863e-29673efc2690 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Graph neural networks for social recommendation

Reference 54

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Toeplitz and circulant matrices: A review

Reference 55

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Observation b0891f45-8314-4dc3-aba6-f31a2d366a91 · outbound

This paper cites Making pre-trained language models better few-shot learners.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Making pre-trained language models better few-shot learners

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.989142Z

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Observation 6c8ff728-15aa-473e-8b6a-7701a471731b · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:33.993396Z

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Garey and David S

Reference 58

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Observation 1ecbba88-bf65-4060-9b8e-d9e50ca7e117 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On computational limits of flowar models: Expressivity and efficiency

Reference 59

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Approximate sparse recovery: optimizing time and measurements

Reference 60

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Observation 4a250b88-9e85-4e28-804f-8ba2bd50de12 · outbound

This paper cites An Over-parameterized Exponential Regression.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform An Over-parameterized Exponential Regression

Reference 61

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Fast quantum algorithm for attention computation

Reference 62

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Differentially private attention computation

Reference 63

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Image restoration by denoising diffusion models with iteratively preconditioned guidance

Reference 64

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Observation c483ccc7-0026-4958-bf27-6fc09da25b0d · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Parameter-Efficient Fine-Tuning with Discrete Fourier Transform

Reference 65

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Observation 39dd12b2-695b-4a71-a962-b5c70418e43b · outbound

This paper cites Apple intelligence foundation language models.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Apple intelligence foundation language models

Reference 66

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks

Reference 67

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Observation 6c5efb70-deb6-4f1b-8681-7f2ae7b33c30 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Lightgcn: Simplifying and powering graph convolution network for recommendation

Reference 68

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Observation c1e0072e-af29-4069-a381-7b63ffd4973e · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Nearly optimal sparse fourier transform

Reference 69

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Observation c783fac0-a316-4d3c-a56b-cc3ef7ac1d0e · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Simple and practical algorithm for sparse fourier transform

Reference 70

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Denoising diffusion probabilistic models

Reference 71

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Observation bbe37768-0ec0-4ad3-a0d7-4629731431fb · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Hyperattention: Long-context attention in near-linear time

Reference 72

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Observation d7e712c3-bfc1-42d0-afb4-1fd9123e8652 · outbound

This paper cites On computational limits of modern hopfield models: A fine-grained complexity analysis.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On computational limits of modern hopfield models: A fine-grained complexity analysis

Reference 73

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform The restricted isometry property of subsampled fourier matrices

Reference 74

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Observation 59a4fb26-7d6c-4d8b-b2b5-4e9539235a9c · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Video diffusion models

Reference 75

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Observation 6ed104f9-4856-4fab-84d8-c9a2588b3734 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Reference 76

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Observation 705aa863-ff36-4c9d-89c2-5abb2f76d41f · outbound

This paper cites Provably optimal memory capacity for modern hopfield models: Tight analysis for transformer-compatible dense associative memories.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Provably optimal memory capacity for modern hopfield models: Tight analysis for transformer-compatible dense associative memories

Reference 77

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Observation 0b04245f-0e75-4ae7-8dde-6fdc21a4e28a · outbound

This paper cites On statistical rates of conditional diffusion transformers: Approximation, estimation and minimax optimality.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On statistical rates of conditional diffusion transformers: Approximation, estimation and minimax optimality

Reference 78

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This paper cites On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs).

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)

Reference 79

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Observation f7a4865c-1ddc-4b4c-8150-bda2148bd14b · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Lo RA : Low-rank adaptation of large language models

Reference 80

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Observation f7709e7a-84c7-4ad2-9ca8-7f40e5eb3d67 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On sparse modern hopfield model

Reference 81

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Observation eb708a22-af10-4698-b27b-3a0074748560 · outbound

This paper cites Bigst: Linear complexity spatio-temporal graph neural network for traffic forecasting on large-scale road networks.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Bigst: Linear complexity spatio-temporal graph neural network for traffic forecasting on large-scale road networks

Reference 82

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Observation 8c80db83-5c30-4971-b7e1-b8106c83cae7 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Sample-optimal fourier sampling in any constant dimension

Reference 83

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Observation 86d76f18-e998-4d4e-b042-cce383d26a43 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform (nearly) sample-optimal sparse fourier transform

Reference 84

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Observation 3385719c-3f7e-4aab-96d0-65cb6612b66c · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On the complexity of k-sat

Reference 85

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Observation e91f2cb5-a697-4b08-aa69-7cd5eb8bf1fb · outbound

This paper cites A robust multi-dimensional sparse fourier transform in the continuous setting.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform A robust multi-dimensional sparse fourier transform in the continuous setting

Reference 86

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Observation 19f87664-30b2-45e1-a553-c3cd2473f37b · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform A faster algorithm for solving general lps

Reference 87

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Observation f1e477ee-d654-4878-8c37-34305a456463 · outbound

This paper cites Sparse fourier transform in any constant dimension with nearly-optimal sample complexity in sublinear time.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Sparse fourier transform in any constant dimension with nearly-optimal sample complexity in sublinear time

Reference 88

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This paper cites Sample efficient estimation and recovery in sparse FFT via isolation on average.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Sample efficient estimation and recovery in sparse FFT via isolation on average

Reference 89

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Sparse Fine-tuning for Inference Acceleration of Large Language Models

Reference 90

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Observation 62950758-e33d-46c2-bbd3-73cd4aa48487 · outbound

This paper cites Reformer: The Efficient Transformer.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Reformer: The Efficient Transformer

Reference 91

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Observation 241d9d1f-b119-4560-9f70-9415970f7203 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On the power of preconditioning in sparse linear regression

Reference 92

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Suprema of chaos processes and the restricted isometry property

Reference 93

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Reference 94

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Observation ea624120-02ad-448a-a74e-a9d23a8666d0 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 95

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This paper cites Dimension-independent sparse fourier transform.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Dimension-independent sparse fourier transform

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:34.162442Z

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Observation 813eb169-f9ba-4a81-8c89-ec33a061710b · outbound

This paper cites Semi-supervised classification with graph convolutional networks.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Semi-supervised classification with graph convolutional networks

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:34.166990Z

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Observation 3a4ce5ee-3a40-4388-92d3-7f27476911c0 · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform On the computational complexity of self-attention

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-15T20:56:34.171125Z

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Observation ad49a63a-769f-46d1-a6e6-b163a6c7d3cd · outbound

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Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform The power of scale for parameter-efficient prompt tuning

Reference 99

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Observation 92e5ae73-38ca-4309-97a6-fc7676341161 · outbound

This paper cites Symbiotic graph neural networks for 3d skeleton-based human action recognition and motion prediction.

Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform Symbiotic graph neural networks for 3d skeleton-based human action recognition and motion prediction

Reference 100

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Pith citing papers

Observation 621ca083-c8d5-43ac-958c-942fd50c1dbb · inbound

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse cites this paper.

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:10:57.183708Z

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Observation dfeadc09-5c90-4514-a781-34d65330052a · inbound

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions cites this paper.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform

Reference 2

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Observation 0cf7b32a-984c-4712-ab44-5f26aab50e0f · inbound

Tracking High-order Evolutions via Cascading Low-rank Fitting cites this paper.

Tracking High-order Evolutions via Cascading Low-rank Fitting Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform

Reference 6

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