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Paper Citation Record · LEDGER

ENA: Efficient N-dimensional Attention

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2508.11921.

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

pith.paper-citation-record.v1
2508.11921 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:49:11.774586Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

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  • verified fuzzy0
  • unresolved18
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef71d323-3fbe-4f32-a902-cccd8ff3a445 · outbound

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

ENA: Efficient N-dimensional Attention Simple linear attention language models balance the recall-throughput tradeoff

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:49:10.064645Z digest=sha256:ce8eae5bf6a8a75e8a2278889a990aab1649ebcbaa5f97f7906d6a8361048430

Observation 7409a641-8b0f-4cc4-b2be-94bdd7918dff · outbound

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

ENA: Efficient N-dimensional Attention Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 5

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source=pdf_text observed=2026-08-05T19:49:10.434135Z digest=sha256:299f8e3815ec6fb0ba4e69986e9b537b28217128036deffe001f07f10054affb

Observation 5932ed19-ccd3-4de4-b488-6cfb120f572a · outbound

This paper cites Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.

ENA: Efficient N-dimensional Attention Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi

Reference 6

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source=pdf_text observed=2026-08-05T19:49:10.529649Z digest=sha256:5c3d60303767426089888004c6e9e4dcc684461ddc807dbd43a19388f6baff32

Observation e1fa40ad-2c2b-4978-9a12-641e8daa6508 · outbound

This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

ENA: Efficient N-dimensional Attention MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:49:10.658797Z digest=sha256:a1e4e5789a0c274ef1e8debe922e309b62bc3b873923ef44c3990393fb2241b8

Observation f2728015-6764-493f-b783-2204843fea74 · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

ENA: Efficient N-dimensional Attention CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:49:10.759726Z digest=sha256:fa25e9ae15cc93f11fa35d7abddf762f18652034619f5ecb087f509cdf257092

Observation f6539178-16bf-4925-a449-451030c5d190 · outbound

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

ENA: Efficient N-dimensional Attention RWKV: Reinventing RNNs for the Transformer Era

Reference 10

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no resolver link, observed 2026-08-05T19:49:10.995232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:49:10.995232Z digest=sha256:fb7621242ee332d8fde0f8fe52ed7bba6737940c65a199ce4e1f55e4b13961a5

Observation e4e509f6-a6d4-4e22-8b4a-d193b14ad195 · outbound

This paper cites RWKV-7 "Goose" with Expressive Dynamic State Evolution.

ENA: Efficient N-dimensional Attention RWKV-7 "Goose" with Expressive Dynamic State Evolution

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:49:11.083164Z digest=sha256:e0b076b46597ee8d6016b82db482a91920c063587ef91cb827354a200616a66e

Observation f577b9ad-6f32-4d67-833c-88464763088f · outbound

This paper cites Benjamin F.

ENA: Efficient N-dimensional Attention Benjamin F

Reference 12

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source=pdf_text observed=2026-08-05T19:49:11.207261Z digest=sha256:33de15b36cc42b6128d13c771ea182fe918d6675fde01f69fcd0f3600b53e424

Observation d826aa2d-4b72-4458-8c27-c16f006d823b · outbound

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

ENA: Efficient N-dimensional Attention Retentive Network: A Successor to Transformer for Large Language Models

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:49:11.286384Z digest=sha256:bf967cb5ad04d0ff9040ccbe18da425082fb0fd5c7b0f2b02905d72d0b478a04

Observation 1520c3a0-57a7-41b3-88de-52608737d10f · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

ENA: Efficient N-dimensional Attention SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 14

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-05T19:49:11.400467Z digest=sha256:6c9a6c7417b56187f02d63e77ff31670681b8c7314c0e8bcf85ee8529d3be2a3

Observation b7f60119-3474-40f2-be41-e244f25896ee · outbound

This paper cites MesaNet: Sequence Modeling by Locally Optimal Test-Time Training.

ENA: Efficient N-dimensional Attention MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

Reference 15

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source=pdf_text observed=2026-08-05T19:49:11.512031Z digest=sha256:c4668570db21d56cef2a101adda0c32e815238ef99c5eef081be1f68a6ae0719

Observation 6e9554dc-f494-46ec-b6c5-da77107e6a44 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

ENA: Efficient N-dimensional Attention CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 16

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source=pdf_text observed=2026-08-05T19:49:11.603155Z digest=sha256:cbcc3af5f8dc71984286cfb7ca4027a69ca05dc4459d794beb030f92f1c663da

Observation 4eff0aab-b8dc-4c9f-bbd0-f285a89a97ba · outbound

This paper cites Fast Video Generation with Sliding Tile Attention.

ENA: Efficient N-dimensional Attention Fast Video Generation with Sliding Tile Attention

Reference 17

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source=pdf_text observed=2026-08-05T19:49:11.691323Z digest=sha256:e25496a859153103063c839c36c32c59ee26995bf3d37ef06dff3bd6682cb9b1

Observation 79275d62-3ddb-431c-8f58-a2206258d72e · outbound

This paper cites DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention.

ENA: Efficient N-dimensional Attention DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention

Reference 18

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:49:11.774586Z digest=sha256:9b6c927d223884facf56a294be4407ac21efc17d20d24b886dc8545a47fb6516

Observation b9275990-0f4d-4869-ae91-60cab976f278 · outbound

This paper cites The Kinetics Human Action Video Dataset.

ENA: Efficient N-dimensional Attention The Kinetics Human Action Video Dataset

Reference 2017

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source=pdf_text observed=2026-08-05T19:49:10.885504Z digest=sha256:93b07a622541e25bab6bdd728d8257825b555c571591711e22186bcf527d6662

Observation 96f147a4-931c-44bc-a728-7df374eb3e08 · outbound

This paper cites Flex Attention: A Programming Model for Generating Optimized Attention Kernels.

ENA: Efficient N-dimensional Attention Flex Attention: A Programming Model for Generating Optimized Attention Kernels

Reference 2023

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source=pdf_text observed=2026-08-05T19:49:10.340227Z digest=sha256:600e50eb4c54d23782d83e70dce3f784bfaaefe4c5196a42bace42ba498f2394

Observation 681a9229-e1f3-4274-be17-a636b7f16f52 · outbound

This paper cites One-Minute Video Generation with Test-Time Training.

ENA: Efficient N-dimensional Attention One-Minute Video Generation with Test-Time Training

Reference 2024

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source=pdf_text observed=2026-08-05T19:49:10.123461Z digest=sha256:e4ccb27097d9fdbe0646ac47ff191975dec415c1cc2267df9293334c58e889dc

Observation 7e023665-2a73-4c80-8a40-9b73275d8718 · outbound

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

ENA: Efficient N-dimensional Attention FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 2025

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source=pdf_text observed=2026-08-05T19:49:10.254232Z digest=sha256:1a4a90ab8e991cfd9103edde3584a2a76af64369bf19b39db30e4f569fe9d7f7

Pith citing papers

No inbound Pith citation observations are available.