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Paper Citation Record · LEDGER

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions

As of 9 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2607.19378.

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

pith.paper-citation-record.v1
2607.19378 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:08:21.412214Z

measured 54 of 54 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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  • malformed identifier1
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Outbound references

Observation 87aa1f7a-c0d6-4b97-b07a-2733ee47099a · outbound

This paper cites Blelloch.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Blelloch

Reference 1

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source=pdf_text observed=2026-08-03T02:08:15.412230Z digest=sha256:31a30a8064310b2f6b204b904ea91cf925206bd299ab2b55c4a7adab54e8a724

Observation 55f6d4b4-72a2-407c-9a2f-c7a270e500bf · outbound

This paper cites Durrant, Jerome Ku, Michael Poli, Greg Brockman, Daniel Chang, Gabriel A.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Durrant, Jerome Ku, Michael Poli, Greg Brockman, Daniel Chang, Gabriel A

Reference 2

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source=pdf_text observed=2026-08-03T02:08:15.631567Z digest=sha256:7a86680326aec0555f01dead4a64ae74d92bf5fe3660a78fb7d5a6bedde3ec30

Observation b46413eb-ed19-47f3-a734-e648bfacaeae · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality

Reference 3

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source=pdf_text observed=2026-08-03T02:08:15.891547Z digest=sha256:7f8a9cdffd5fd52ad3fb1505f06d5fada0163f8d653858a1ffe77666a84ca9e1

Observation 37d448e0-890a-4cdd-aca0-17b1a76004d2 · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 4

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source=pdf_text observed=2026-08-03T02:08:15.979574Z digest=sha256:31d7436be708d433d5c5f1904a4c97c3ab3f00a5a213961565b7b315738cbbc9

Observation 23ee9d2c-81c0-46eb-add9-44a41eba1ad4 · outbound

This paper cites Vision transformers need registers, 2023.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Vision transformers need registers, 2023

Reference 5

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source=pdf_text observed=2026-08-03T02:08:16.061370Z digest=sha256:1f42b99197c3653219c3c2ea8fbe53b93fab6243868bf5ee62c78777efe9ac13

Observation 0ab9a9c4-8a26-4f82-8320-51c9d7023f07 · outbound

This paper cites ImageNet: A large-scale hierarchical image database.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions ImageNet: A large-scale hierarchical image database

Reference 6

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source=pdf_text observed=2026-08-03T02:08:16.142643Z digest=sha256:c638a42b9bacc47c51a46af09e7fd0a4c6be6cbab6ddfc10f62485a20a9356cc

Observation 19faf518-40d4-43dd-99e5-e1c56eb9fca0 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions An image is worth 16x16 words: Transformers for image recognition at scale

Reference 7

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source=pdf_text observed=2026-08-03T02:08:16.221668Z digest=sha256:2ed206640078f47f121d725e4b1392e58cc9db2ada1a69a7df8929f224889ff1

Observation db771726-44c8-46b5-98bd-cafd02e506b2 · outbound

This paper cites Hungry hungry hippos: Towards language modeling with state space models.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Hungry hungry hippos: Towards language modeling with state space models

Reference 8

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source=pdf_text observed=2026-08-03T02:08:16.286688Z digest=sha256:09426f52b9bcfe4912bd05fafd08abfe5b92c8e1b3525d64d27b1f0ece642563

Observation 4857b46d-10a6-41f8-981e-e21f75edd5f9 · outbound

This paper cites Fu, Hermann Kumbong, Eric Nguyen, and Christopher R´e.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Fu, Hermann Kumbong, Eric Nguyen, and Christopher R´e

Reference 9

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source=pdf_text observed=2026-08-03T02:08:16.346641Z digest=sha256:1db420ec76cfe0a174dd1f8732787f558086e5c0a981092c63cf97e6a9519cba

Observation 00793727-eb64-4ca8-aa93-d9937d885065 · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 10

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source=pdf_text observed=2026-08-03T02:08:16.411939Z digest=sha256:b7dd42f3bbfbcfe3685c545121f0e53525e23f69b4977e442a5037f58fd2cc35

Observation 57624d5e-ce4a-4e16-8e9c-d481a5ddad3c · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Efficiently modeling long sequences with structured state spaces

Reference 11

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source=pdf_text observed=2026-08-03T02:08:16.474740Z digest=sha256:e3e79ad1fecb3f183b5e258bd617b414b8e2164d0b4bd6c9cb3412011e39c118

Observation a1860729-571e-4f98-b67f-1d428712cd37 · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 12

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source=pdf_text observed=2026-08-03T02:08:16.534837Z digest=sha256:86983567eed740a40c82fbd69aa3e95c836f164d0ab95e223ead5d8daca361b7

Observation 26c436a0-943a-48f0-9559-b779b7c99d9e · outbound

This paper cites Squeeze-and-excitation networks.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Squeeze-and-excitation networks

Reference 13

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source=pdf_text observed=2026-08-03T02:08:16.598392Z digest=sha256:04a434db416c7ca1b5bfffa35e92e1d95c7f3ab89d8ff19d9a2525b95db88af0

Observation 9f1a3c19-4088-4af9-afd5-874605a6ca68 · outbound

This paper cites John, Dejun Lin, Polina Binder, Malcolm Greaves, Vega Shah, John St.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions John, Dejun Lin, Polina Binder, Malcolm Greaves, Vega Shah, John St

Reference 14

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source=pdf_text observed=2026-08-03T02:08:16.621455Z digest=sha256:45395fa9909bfbe1816459bc112afe28e25eea9ff0e0fc7d0b1f0d8b7cee96c8

Observation 5236a01a-5997-4834-8c89-78c2e36e8d37 · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 15

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source=pdf_text observed=2026-08-03T02:08:16.752001Z digest=sha256:5d8e5f37f99171e736f26e9c55f23ef6fd95dffdf0d020f5ae38b3c343b8a9fe

Observation 84566fb7-414f-4f06-bbdf-6b75fe2f65fa · outbound

This paper cites Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 16

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source=pdf_text observed=2026-08-03T02:08:16.871787Z digest=sha256:e2bd186c03676574acd8499498fc546cbac536f5e81256ce62e2ba717160014a

Observation fdfa2ede-d9a8-4836-b37c-c31fd828dac7 · outbound

This paper cites Mamba-ND: Selective state space modeling for multi-dimensional data.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Mamba-ND: Selective state space modeling for multi-dimensional data

Reference 17

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source=pdf_text observed=2026-08-03T02:08:16.982851Z digest=sha256:bcc7cb2d771edde88125cbd1d47f0d56d08222f7c2255c9839f4c100be623721

Observation 7d2d767a-49d3-4e5b-afd4-9e0a7b8aeca1 · outbound

This paper cites Pants: The pancreatic tumor segmentation dataset.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Pants: The pancreatic tumor segmentation dataset

Reference 18

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source=pdf_text observed=2026-08-03T02:08:17.204487Z digest=sha256:6d9250e24a54514a73d14829a92f529e5ed512b23ac6a6dec100a069b64ffbb8

Observation c7c69309-3241-4a3c-80c9-d3d056e5fa99 · outbound

This paper cites VMamba: Visual State Space Model.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions VMamba: Visual State Space Model

Reference 19

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source=pdf_text observed=2026-08-03T02:08:17.329374Z digest=sha256:37d0013c230644db792b84cf889252fd59f66dadf2dbc777cd6838af779e4684

Observation 02e81345-eee2-4e7b-a7c1-4816b96bdcb0 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Swin transformer: Hierarchical vision transformer using shifted windows

Reference 20

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Observation ff347d29-d375-47c1-bf89-ced36fdaab69 · outbound

This paper cites A convnet for the 2020s.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions A convnet for the 2020s

Reference 21

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source=pdf_text observed=2026-08-03T02:08:17.563421Z digest=sha256:6337b6653ffc14b9f58781eed1f9a723989e043693fa9fd2d274aa775fb32e39

Observation d2a48757-7987-4bed-aa63-e9a11fd7be3d · outbound

This paper cites Downs, Preey Shah, Tri Dao, Stephen A.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Downs, Preey Shah, Tri Dao, Stephen A

Reference 22

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source=pdf_text observed=2026-08-03T02:08:17.704379Z digest=sha256:bcd3b680c9cdb0686471cdb14669fad86265e09c2dc55c6daa2f093133da6e9a

Observation 93dfb0d1-6624-4231-a645-d2ce6f0f4562 · outbound

This paper cites Durrant, Brian Kang, Dhruva Katrekar, David B.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Durrant, Brian Kang, Dhruva Katrekar, David B

Reference 23

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source=pdf_text observed=2026-08-03T02:08:17.823373Z digest=sha256:54a619b2d7e35e197069f34fa7b093c45390c49c3b4acf2ac12c15a509bb9605

Observation 0415d342-0cdc-45ce-89d2-07294182b10c · outbound

This paper cites NVIDIA Tesla V100 GPU Architecture.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions NVIDIA Tesla V100 GPU Architecture

Reference 24

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Observation 9cc434ab-ca19-4e6f-9e06-2bc644fa5ba2 · outbound

This paper cites NVIDIA A100 Tensor Core GPU Architecture.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions NVIDIA A100 Tensor Core GPU Architecture

Reference 25

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Observation 36efce5e-dbbe-4751-a841-e97c3217e94c · outbound

This paper cites NVIDIA H100 Tensor Core GPU Architecture.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions NVIDIA H100 Tensor Core GPU Architecture

Reference 26

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Observation 7f72ecd3-daf7-40ed-987c-f31878fda615 · outbound

This paper cites NVIDIA Blackwell Architecture Technical Overview.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions NVIDIA Blackwell Architecture Technical Overview

Reference 27

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source=pdf_text observed=2026-08-03T02:08:18.367478Z digest=sha256:46902f2aac7c4b9e8476e121aefe584b1f50665531088d5393fd2ba2b00d27ff

Observation be4c17d6-2a38-4f84-bf83-24942b662d32 · outbound

This paper cites NVIDIA Corporation, cuda toolkit 13.2; cufft 12.2.0.46 edition, 2026.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions NVIDIA Corporation, cuda toolkit 13.2; cufft 12.2.0.46 edition, 2026

Reference 28

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source=pdf_text observed=2026-08-03T02:08:18.483933Z digest=sha256:cd2add66ef0c986f5702bb3b9fa4cf33337d61fb5a88adaec482b24630f5ff5a

Observation de853397-183b-4605-9ec5-134c63d56ea2 · outbound

This paper cites NVIDIA Corporation, mathdx 26.03.0 (cufftdx 1.7.0) edition, 2026.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions NVIDIA Corporation, mathdx 26.03.0 (cufftdx 1.7.0) edition, 2026

Reference 29

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source=pdf_text observed=2026-08-03T02:08:18.572852Z digest=sha256:882fe6cb17bdd38a48b577c5c769426f374acf4614ba1c9a16ba609dd1a5f04e

Observation 6e177abd-29c2-43e6-b74a-1997a6700356 · outbound

This paper cites CUTLASS: CUDA Templates for Linear Algebra Subroutines.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions CUTLASS: CUDA Templates for Linear Algebra Subroutines

Reference 30

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source=pdf_text observed=2026-08-03T02:08:18.723822Z digest=sha256:3583c47edc5975232cc8610d553fe5b1c974affa681686eabbe5ecce4e704888

Observation e1b342d2-0652-4418-a921-71a58b5bc3df · outbound

This paper cites Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B

Reference 31

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source=pdf_text observed=2026-08-03T02:08:18.831168Z digest=sha256:f6d6ce65a6775e399e4eed526920310da47e8724e313b4ec98eb2e1079465636

Observation ba0d86c9-dc78-444c-81af-3d1a4c9b4bc4 · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Rwkv: Reinventing rnns for the transformer era

Reference 32

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source=pdf_text observed=2026-08-03T02:08:18.941960Z digest=sha256:99f742dda2aeea8186f73db7e7f3206c1ea411011dfe466a13942409cbbc9139

Observation 5d1f5520-7a07-45ef-bff9-eb1696815c2e · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions FiLM: Visual reasoning with a general conditioning layer

Reference 33

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source=pdf_text observed=2026-08-03T02:08:19.093636Z digest=sha256:5fcec908704c8066e6104f907bd5a8d2cb14ecf6867ae10efdba64a35b4bc8ff

Observation b5bcef22-69a5-4ae5-bb6a-170f978f5a8c · outbound

This paper cites Hyena hierarchy: Towards larger convolutional language models.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Hyena hierarchy: Towards larger convolutional language models

Reference 34

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source=pdf_text observed=2026-08-03T02:08:19.259675Z digest=sha256:fe140db5844197ec1ed1fecf18a89da4f34f1373ca94d193b72fb814f36574d4

Observation 0a6f922d-b81c-4723-80e9-a4a7c4290ce5 · outbound

This paper cites Romero, Robert-Jan Bruintjes, Jakub M.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Romero, Robert-Jan Bruintjes, Jakub M

Reference 35

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source=pdf_text observed=2026-08-03T02:08:19.394147Z digest=sha256:eda5f23d1d89a2fddd311ee28bacad5a9015eccaec30a8d1bd5038da53f37f35

Observation 1c30d82f-b4b6-45a7-9dbc-1346ec8c76bb · outbound

This paper cites Romero, Anna Kuzina, Erik J.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Romero, Anna Kuzina, Erik J

Reference 36

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source=pdf_text observed=2026-08-03T02:08:19.572572Z digest=sha256:2ccca292c08fe0d95f53f1f373da93cbf81b2c9ee3918afd6b66ae4b96f2cd82

Observation b8de0321-fdfd-4142-bf3c-18d99a04f15e · outbound

This paper cites Implicit neural representations with periodic activation functions.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Implicit neural representations with periodic activation functions

Reference 37

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source=pdf_text observed=2026-08-03T02:08:19.678096Z digest=sha256:a8cddabbf0499bd88b3cfb03a81e55193e683f2208601143398ccff78582f670

Observation ef3b4f13-472d-4eb6-ab68-a7b24e5bb8a3 · outbound

This paper cites Smith, Andrew Warrington, and Scott W.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Smith, Andrew Warrington, and Scott W

Reference 38

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source=pdf_text observed=2026-08-03T02:08:19.769129Z digest=sha256:0eb82883f42d5bf31a6d7aeb3a73a76d48cda319b3c294724a23310d5ecf0601

Observation 8ad4192c-0d4a-4151-8537-17d7414592ea · outbound

This paper cites HyenaPixel: Global image context with convolutions, 2024.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions HyenaPixel: Global image context with convolutions, 2024

Reference 39

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source=pdf_text observed=2026-08-03T02:08:19.900473Z digest=sha256:6fa84c1187d8507aba4f28e8214e19c171cf233aca4b25724380283710da679d

Observation b3c1b9e1-1fcc-435d-a873-759f0bdb9fcc · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Retentive Network: A Successor to Transformer for Large Language Models

Reference 40

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source=pdf_text observed=2026-08-03T02:08:20.068863Z digest=sha256:1826df462684dfd53a9b0559b3fe5baed8ca8232b1f645a65c1247e81868671c

Observation 948613b1-a859-4158-8167-07321c08220c · outbound

This paper cites Spike No More: Stabilizing the Pre-training of Large Language Models.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Spike No More: Stabilizing the Pre-training of Large Language Models

Reference 41

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source=pdf_text observed=2026-08-03T02:08:20.137063Z digest=sha256:f4c138681887148d0462ef8254248b4cf64e5e01cd2757e2d1245d27d69d0eff

Observation 432cb132-374e-4e15-b6a4-79b5b8590cae · outbound

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

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Kimi Linear: An Expressive, Efficient Attention Architecture

Reference 42

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source=pdf_text observed=2026-08-03T02:08:20.290764Z digest=sha256:3110a5611ad5e9c1dd600a9f8b28359eaf02eeca5fa75fb0cf86465e52585c8f

Observation d13a5313-4660-4d71-9b9d-24cc2550bcdf · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Training data-efficient image transformers & distillation through attention

Reference 43

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source=pdf_text observed=2026-08-03T02:08:20.436823Z digest=sha256:eb846ee571a6e20b26311b54b0d0bfa5a5f6ac88dd68b82b670e59350b3f3ed4

Observation 14d0376b-1d19-4a20-9871-8a6c3e069f2b · outbound

This paper cites Attention is all you need.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Attention is all you need

Reference 44

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source=pdf_text observed=2026-08-03T02:08:20.482985Z digest=sha256:fa8964a7e37d33b9adafc73b3cb0b17c336040dde2cfbf600bf168f4dfd3a354

Observation a32cc6df-cc8a-48b6-b8e9-2eaf54eefdb6 · outbound

This paper cites Scaling laws in patchification: An image is worth 50,176 tokens and more.arXiv preprint arXiv:2502.03738, 2025.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Scaling laws in patchification: An image is worth 50,176 tokens and more.arXiv preprint arXiv:2502.03738, 2025

Reference 45

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source=pdf_text observed=2026-08-03T02:08:20.540911Z digest=sha256:d5ac8494fc49cf26e8a1766d7711a82dfe38cf9c8a9d7bb64d77173eca780eb3

Observation b56f1f2c-6713-4717-8b4d-c90704367993 · outbound

This paper cites Vit-5: Vision transformers for the mid-2020s.arXiv preprint arXiv:2602.08071, 2026.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Vit-5: Vision transformers for the mid-2020s.arXiv preprint arXiv:2602.08071, 2026

Reference 46

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source=pdf_text observed=2026-08-03T02:08:20.637475Z digest=sha256:f07ab738d5d8d3f000261143b1223c260cd9fecf472504fa1711c32e9c51174a

Observation 5f979af4-c2d0-4d53-946f-4386eea6ed69 · outbound

This paper cites Resnet strikes back: An improved baseline in 2.5 lines of code, 2021.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Resnet strikes back: An improved baseline in 2.5 lines of code, 2021

Reference 47

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source=pdf_text observed=2026-08-03T02:08:20.750558Z digest=sha256:0a58b36ba452570cc70390542c2e1a371f943ac129e076ee383a0b0dfe0e02fe

Observation 82e06fde-a4e6-4fc5-8c02-a34f0d9b84d8 · outbound

This paper cites Gated delta networks: Improving mamba2 with delta rule.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Gated delta networks: Improving mamba2 with delta rule

Reference 48

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source=pdf_text observed=2026-08-03T02:08:20.824749Z digest=sha256:daa6c3ec55d0402a5d34c0ffa7fe452895110dcf7ba310a5246a4c1128a2d118

Observation 1c73424b-bb60-427f-a488-908d29fdba0f · outbound

This paper cites Flashattention-4: Algorithm and kernel pipelining co-design for asymmetric hardware scaling,.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Flashattention-4: Algorithm and kernel pipelining co-design for asymmetric hardware scaling,

Reference 49

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source=pdf_text observed=2026-08-03T02:08:20.933776Z digest=sha256:4915972a19b4303b03d608e2dc537ffc400801a5ad26a797763313b7ec36c225

Observation 1456c8ee-e7da-4426-b001-f65627f8c4fd · outbound

This paper cites Root mean square layer normalization.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Root mean square layer normalization

Reference 50

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source=pdf_text observed=2026-08-03T02:08:21.086728Z digest=sha256:017bd6c89d35549221a3982a59f78649b1ae179f4cf54880383b09827df3bb04

Observation 9779b8fa-64fe-4ce8-9701-0ef87aac8899 · outbound

This paper cites Squeeze-and-attention networks for semantic segmentation.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Squeeze-and-attention networks for semantic segmentation

Reference 51

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source=pdf_text observed=2026-08-03T02:08:21.180394Z digest=sha256:dcb0dd1f84778154b430690b156575c114994b9ed32c72c23f177b5e8ea97292

Observation 54a77d2b-990c-4d4c-a4cb-6904a309deb1 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 52

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source=pdf_text observed=2026-08-03T02:08:21.294067Z digest=sha256:e8e1568ce360a169f06af0a2deb451a25fe050db847da753ed9e7a2ba4eced1a

Observation 6c608dad-6f69-41bf-ad8c-b1c17f9ba78d · outbound

This paper cites Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale,.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale,

Reference 53

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source=pdf_text observed=2026-08-03T02:08:21.412214Z digest=sha256:cf631cae1616aa76ef314d855e91bfe9c1b68d029280ae86fe8e1e8c5256a542

Observation 811fbc0b-9fc6-4448-93a0-948e7f300605 · outbound

This paper cites an unresolved cited work.

Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions Unresolved cited work

Reference 2026

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source=pdf_text observed=2026-08-03T02:08:21.026402Z digest=sha256:77203395748d49a08bcc7fa1a50913aa5e3b7e69bfa382d99d429a3a1b796837

Pith citing papers

No inbound Pith citation observations are available.