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Paper Citation Record · LEDGER

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones

As of 8 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2607.17419.

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

pith.paper-citation-record.v1
2607.17419 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T18:06:56.342744Z

measured 67 of 67 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

67 of 67 outbound references displayed

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  • verified fuzzy0
  • unresolved66
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f243787-640d-4949-b53d-bbe6544ae9f7 · outbound

This paper cites Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pages =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pages =

Reference 1

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Observation 8561567c-1148-4b58-8b74-7905142eab7b · outbound

This paper cites Transformers are RNNs: fast autoregressive transformers with linear attention , year =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Transformers are RNNs: fast autoregressive transformers with linear attention , year =

Reference 2

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source=arxiv_source observed=2026-08-01T18:06:48.683349Z digest=sha256:bfb33b12969e98e5fcb769239f980e680b8b0dae4d656d71615282b28f6de405

Observation 3bf73d91-3635-400a-acfe-bd3472daf7de · outbound

This paper cites Advances in neural information processing systems , volume=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Advances in neural information processing systems , volume=

Reference 3

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source=arxiv_source observed=2026-08-01T18:06:48.778220Z digest=sha256:b60b3d6338bcdae4ea021414c855ac869ac687c3fe14ec566da10869fdb5296f

Observation 86d98c18-52eb-4932-92da-eaadab7a7c71 · outbound

This paper cites Repeat After Me: Transformers are Better than State Space Models at Copying.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Repeat After Me: Transformers are Better than State Space Models at Copying

Reference 4

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source=arxiv_source observed=2026-08-01T18:06:48.827702Z digest=sha256:d1b9626eb3d1f0db3ca0d5117aaa81d725e54f66fe91ffab8d813fda0b2ba65a

Observation df674fbe-a514-4f02-98a8-2e2d0c8aaf60 · outbound

This paper cites arXiv preprint arXiv:2502.13685 , year=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones arXiv preprint arXiv:2502.13685 , year=

Reference 5

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source=arxiv_source observed=2026-08-01T18:06:48.911567Z digest=sha256:39f9b046c0abb8ba6ae0175d3534fe5aee52661b69ba9092133260b1846d83fa

Observation a105e73d-5409-48ef-93d3-0e172810b052 · outbound

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

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Retentive Network: A Successor to Transformer for Large Language Models

Reference 6

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Observation 0d7376ba-0539-4024-aafb-acac5f8f53a9 · outbound

This paper cites International Conference on Learning Representations , year=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones International Conference on Learning Representations , year=

Reference 7

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Observation 5683e727-bf51-4783-9163-e745329bb6e0 · outbound

This paper cites Linear Transformers Are Secretly Fast Weight Programmers , booktitle =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Linear Transformers Are Secretly Fast Weight Programmers , booktitle =

Reference 8

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source=arxiv_source observed=2026-08-01T18:06:49.273536Z digest=sha256:9668892716576c838435d61075632e07438c760ecb41ca3bce6cc77d82dfb902

Observation 22e4d873-59a4-4713-bf8c-0c708b73d4d1 · outbound

This paper cites International Conference on Learning Representations , year=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones International Conference on Learning Representations , year=

Reference 9

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Observation 1d819147-8faf-4ead-a132-95fcee381911 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , articleno =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Proceedings of the 41st International Conference on Machine Learning , articleno =

Reference 10

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Observation 99656b9e-84f9-414c-9f7c-39a503c42eea · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , articleno =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Proceedings of the 41st International Conference on Machine Learning , articleno =

Reference 11

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Observation 93df1d03-2f50-4297-9a87-224210c23e8e · outbound

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

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Parallelizing Linear Transformers with the Delta Rule over Sequence Length , url =

Reference 12

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Observation 7afa7047-622a-4878-a55c-03fc4dc33eef · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones The Thirteenth International Conference on Learning Representations , year=

Reference 13

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Observation 90fd81d2-a94f-4806-9a67-b59af8170f79 · outbound

This paper cites SIAM Journal on Scientific and Statistical Computing , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones SIAM Journal on Scientific and Statistical Computing , volume =

Reference 14

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Observation d2bea531-8d76-4a98-ae55-5a9ec3acb5f6 · outbound

This paper cites American Journal of Mathematics , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones American Journal of Mathematics , volume =

Reference 15

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Observation a2f044aa-57d8-4571-b729-bed956f45b6f · outbound

This paper cites an unresolved cited work.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Unresolved cited work

Reference 16

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Observation f3bb7302-eb9c-45fe-87d7-2970b825c95f · outbound

This paper cites Analysis on Symmetric Cones , series =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Analysis on Symmetric Cones , series =

Reference 17

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Observation efdc987c-f1ce-4742-a6bf-f0a7bc158e3c · outbound

This paper cites and Majumdar, Satya N.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones and Majumdar, Satya N

Reference 18

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Observation 5b014494-c39b-4291-8b39-aa4126742201 · outbound

This paper cites Israel Journal of Mathematics , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Israel Journal of Mathematics , volume =

Reference 19

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Observation 993372cf-07e4-4088-86b5-d31b9b73ed02 · outbound

This paper cites Hopfield Networks Is All You Need , booktitle =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Hopfield Networks Is All You Need , booktitle =

Reference 20

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Observation 839db26a-4311-4cdc-92ca-ca2501ed38bb · outbound

This paper cites , title =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones , title =

Reference 21

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Observation 7cae7320-66c8-46f0-95da-fd8b646a451a · outbound

This paper cites On a Model of Associative Memory with Huge Storage Capacity , journal =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones On a Model of Associative Memory with Huge Storage Capacity , journal =

Reference 22

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Observation f54ea753-a79b-4721-90c9-ab9c0e0a2d88 · outbound

This paper cites First Conference on Language Modeling (.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones First Conference on Language Modeling (

Reference 23

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Observation 579e1025-61d7-40cf-a056-0032bbf7d3a9 · outbound

This paper cites Findings of the Association for Computational Linguistics:.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Findings of the Association for Computational Linguistics:

Reference 24

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Observation f2d1598d-99be-4603-b1e7-77f6745e6fe9 · outbound

This paper cites and Kong, Lingpeng , title =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones and Kong, Lingpeng , title =

Reference 25

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Observation 2237979d-7cff-42c2-8038-6902b1201aad · outbound

This paper cites The Tenth International Conference on Learning Representations (.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones The Tenth International Conference on Learning Representations (

Reference 26

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Observation df390231-abcb-4d22-ac83-8e152c14974b · outbound

This paper cites Transformer Circuits Thread , year =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Transformer Circuits Thread , year =

Reference 27

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Observation 866056da-88a2-4898-9c64-fcb3b4d6f22e · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Unresolved cited work

Reference 28

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Observation 40b13eaf-b4db-41fd-812f-aabf1e054584 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones , title =

Reference 29

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Observation 60f6c8f3-3d7d-4c49-acab-34fae7611585 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones , title =

Reference 30

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Observation 742fb5cf-0969-4d8d-afe5-f2fc0d02ccaf · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones , title =

Reference 31

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Observation b57aca37-303e-47ba-bb31-2263a2a427e8 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones , title =

Reference 32

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Observation db4ea769-0503-4da2-8433-aaed46616ad8 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2023 , eprint=

Reference 33

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Observation 29ea9a28-8101-4065-a007-7b9516d5cee2 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2025 , eprint=

Reference 34

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Observation a5e45b83-f51d-477f-a082-c1a1c9799930 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Advances in Neural Information Processing Systems (NeurIPS) , year=

Reference 35

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Observation 30db7f6d-37c1-48b8-8c43-396adf83397d · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2024 , eprint =

Reference 36

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Observation 083ab70e-bdda-40b3-b9ea-07764fe13bf9 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2024 , eprint =

Reference 37

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Observation c0deb284-9fd2-494c-b53f-eb56c33abbc2 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones arXiv preprint arXiv:2505.23666 , year=

Reference 38

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Observation 45e90589-eb4f-4c15-937b-48941207c534 · outbound

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Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2025 , eprint=

Reference 39

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source=arxiv_source observed=2026-08-01T18:06:53.000682Z digest=sha256:c03f8fa3dba2e6cb2fcf589bdc98fdb93015757101dc5e2ce979b2154f8aeaed

Observation f991e379-4849-4a2c-b418-3d77cc2a657f · outbound

This paper cites arXiv preprint arXiv:2510.01450 , year=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones arXiv preprint arXiv:2510.01450 , year=

Reference 40

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source=arxiv_source observed=2026-08-01T18:06:53.047330Z digest=sha256:ee50e56c33052c30e54dd88fcb8915fac989b8b795766df3a9e449156e5beec3

Observation dcd6c85e-7039-4e3f-8195-9aad8580147f · outbound

This paper cites Annals of Physics , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Annals of Physics , volume =

Reference 41

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source=arxiv_source observed=2026-08-01T18:06:53.126299Z digest=sha256:1727fab7803de0c73c730cd622239e81a5e43d6b8100ba2db4ae1ccad8132253

Observation 2454b2ad-9134-48f2-a8fb-49708ebd909f · outbound

This paper cites Algorithmica , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Algorithmica , volume =

Reference 42

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source=arxiv_source observed=2026-08-01T18:06:53.214353Z digest=sha256:74ce7bd5b0f0a24bcbe10c7fa3ed8cb7324edab679bd268cc857f2cc7a060ade

Observation 83bbeb46-4588-4e9f-b44d-09962ed95e5f · outbound

This paper cites Journal of Complexity , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Journal of Complexity , volume =

Reference 43

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source=arxiv_source observed=2026-08-01T18:06:53.314652Z digest=sha256:9e9627ddd9c6d5def05828cf6249e6c4247ea429d65f5d3a4bb37a963d3401a9

Observation da153f5f-c4df-45cc-950e-c2fb3c9bb4f2 · outbound

This paper cites 2024 , url =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2024 , url =

Reference 44

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source=arxiv_source observed=2026-08-01T18:06:53.399722Z digest=sha256:db59c35f1e9e304b346ed8c96b7b629476afe49752f6d6caff18a94f8263809e

Observation 89b98638-f75a-4fdd-818e-817e75c60638 · outbound

This paper cites Proceedings of the 39th International Conference on Machine Learning (.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Proceedings of the 39th International Conference on Machine Learning (

Reference 45

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source=arxiv_source observed=2026-08-01T18:06:53.459263Z digest=sha256:f006067da3c89dd8e0e047c523545ab13c7194c1c5933c762e2bdb903ab12b51

Observation e5f4f255-026c-4e03-b9ce-d24e33baa975 · outbound

This paper cites 2025 , eprint=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2025 , eprint=

Reference 46

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source=arxiv_source observed=2026-08-01T18:06:53.599165Z digest=sha256:aab24612c4d235e68395ebbb7a58680f067db12b608907efa9a66dc8db4aa91c

Observation 65bc154a-0a18-4053-a210-f7a817e66eee · outbound

This paper cites 2025 , eprint=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2025 , eprint=

Reference 47

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source=arxiv_source observed=2026-08-01T18:06:53.827495Z digest=sha256:e0c9f35d112929ee1497297c98d33b907dc49230e23b6246b271cf2daf32aa86

Observation 429795ac-459b-4eea-953f-3ce05fe8f68a · outbound

This paper cites 2026 , eprint=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2026 , eprint=

Reference 48

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source=arxiv_source observed=2026-08-01T18:06:54.053143Z digest=sha256:4e111a6cfbf62fddfcb0f00a02ef1bd0426475d8fc1b88e86c8abb79c19936d8

Observation 875d758a-f54b-447f-a6c6-ac08efe1a7f3 · outbound

This paper cites Differential Transformer.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Differential Transformer

Reference 49

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source=arxiv_source observed=2026-08-01T18:06:54.224936Z digest=sha256:04fb1da363b74afd0b6810736f5792f6aa89b1b22f91baf5b12ae2d63be9b2f3

Observation dd125648-1291-4896-b8a0-bec78cb275bc · outbound

This paper cites Copy Suppression: Comprehensively Understanding an Attention Head.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Copy Suppression: Comprehensively Understanding an Attention Head

Reference 50

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source=arxiv_source observed=2026-08-01T18:06:54.358789Z digest=sha256:c6b0ebef4eba0c6dd8deadfc320233014ee8e628a20d853b20b0683960ab3d14

Observation 9d0894d7-d7f6-4bd8-9b9b-d9c088546a0d · outbound

This paper cites Selective Attention Improves Transformer.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Selective Attention Improves Transformer

Reference 51

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source=arxiv_source observed=2026-08-01T18:06:54.522323Z digest=sha256:b4e78268e1d8488c61f87cff444b7d83f11af342e482febe60056fccc15e8b4c

Observation ff0e3634-e639-437f-975f-0f6ea85dcbd6 · outbound

This paper cites More Expressive Attention with Negative Weights.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones More Expressive Attention with Negative Weights

Reference 52

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source=arxiv_source observed=2026-08-01T18:06:54.696636Z digest=sha256:490f6cb0540c10ef1df5f7fae5b1573b9319a1cc27b456a9f30f3dcb4ffa2e74

Observation 1cc921e6-66f5-4dda-a535-8bfa71653758 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Efficient Streaming Language Models with Attention Sinks

Reference 53

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source=arxiv_source observed=2026-08-01T18:06:54.835682Z digest=sha256:47367be6d574234209b805f63903dc5eab4279e66ee9b28017b3b3c6b5c4296f

Observation 822ff1e7-3b2e-4886-8d99-4a2f91d58481 · outbound

This paper cites Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth

Reference 54

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source=arxiv_source observed=2026-08-01T18:06:54.962658Z digest=sha256:ba037df5b5573a3be9162c0df66f484c79a44ae212819ed7cfccd0be6ab74ed5

Observation 6aae4ba4-0cf4-4087-adca-d5c9f8721e2f · outbound

This paper cites First Conference on Language Modeling , year=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones First Conference on Language Modeling , year=

Reference 55

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source=arxiv_source observed=2026-08-01T18:06:55.112994Z digest=sha256:97651b6dc0fa2a001995be8b0b1f47e29e72e71fdd070c01c7560faf1b166f8b

Observation 7c298b26-34bb-40fe-9ade-e23b8acdb5b3 · outbound

This paper cites 2023 , howpublished=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2023 , howpublished=

Reference 56

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source=arxiv_source observed=2026-08-01T18:06:55.185689Z digest=sha256:7640b007f2d5419781eef3e30b39797c077f1c33046867ac9565886f75c2826c

Observation bc11385f-1a5c-4f94-9b06-9cd462ba9d62 · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones TinyLlama: An Open-Source Small Language Model

Reference 57

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source=arxiv_source observed=2026-08-01T18:06:55.271436Z digest=sha256:ed688b222661848d54248d36dbc575ce9ab710a6ab4a8efc23a667544d2ba573

Observation ccf54976-ad2a-432e-81aa-844e83e1f765 · outbound

This paper cites 2016 , eprint =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2016 , eprint =

Reference 58

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source=arxiv_source observed=2026-08-01T18:06:55.371466Z digest=sha256:e18cd4c9e5fc9b66fb6f95be3ee909b6b2dfb2b6bff15f6fbe6cba8adf9e678e

Observation d980b704-111a-4c47-b459-53866248109f · outbound

This paper cites 1993 , issn =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 1993 , issn =

Reference 59

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verified exact
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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-01T18:06:55.528144Z digest=sha256:20cd51f67c0094074ef60032255eb110f0cce1dc60f82023b774100e1788e365

Observation 407319d9-a453-4a3f-98c1-e8eae492fda1 · outbound

This paper cites Physical Review Letters , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Physical Review Letters , volume =

Reference 60

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source=arxiv_source observed=2026-08-01T18:06:55.639072Z digest=sha256:d998c4f675457609591893feed668ffbc6f08e73ccb977fac2ce04f2e38a4ecb

Observation e9666520-d856-4334-9e4a-f9b4c294938b · outbound

This paper cites 2026 , eprint =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2026 , eprint =

Reference 61

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source=arxiv_source observed=2026-08-01T18:06:55.728064Z digest=sha256:5593dd153c458ca7991dafab00ab391c1d5025329eb4f27b160fde5453ed891e

Observation 170521ef-5649-4364-bc8c-08f7b7eb98ff · outbound

This paper cites and Lindenstrauss, Joram , title =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones and Lindenstrauss, Joram , title =

Reference 62

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source=arxiv_source observed=2026-08-01T18:06:55.840416Z digest=sha256:365e93ca9b362c014459f21db055b696d7b95591386f8a1d1aa4d781e60e01b5

Observation 178fb62d-6981-47eb-957a-2835c080c40f · outbound

This paper cites Journal of Computer and System Sciences , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Journal of Computer and System Sciences , volume =

Reference 63

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source=arxiv_source observed=2026-08-01T18:06:55.988053Z digest=sha256:6ef1c8beda0b9184ef736df137a41efa9c97e4613d5b442c566e94bb950a81d1

Observation a29d6b50-d049-4c11-8816-9be5bd4e87e9 · outbound

This paper cites Combinatorics, Probability and Computing , volume =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones Combinatorics, Probability and Computing , volume =

Reference 64

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source=arxiv_source observed=2026-08-01T18:06:56.062522Z digest=sha256:13d279f566874adac6d16d57cd39e363689e8222e61f80e7fe5a8fe5ad52d44b

Observation 02582b04-9665-4a71-99b4-626511c535ac · outbound

This paper cites and Elad, Michael , title =.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones and Elad, Michael , title =

Reference 65

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source=arxiv_source observed=2026-08-01T18:06:56.121665Z digest=sha256:091a4bb8f3ca8cc4d7fe439009a8285d8c6f4a53e7d0a866f66b0afa6226bb51

Observation bf6b149d-75c0-4cd4-be5d-8a77ebcc2222 · outbound

This paper cites 2025 , eprint=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2025 , eprint=

Reference 66

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source=arxiv_source observed=2026-08-01T18:06:56.232421Z digest=sha256:bacd673db13b35e135a5d527ffaafd62d7a8e7900829ae9da79e67a2589bfd06

Observation b9978a0a-6b71-4930-b568-2b7e9acb5510 · outbound

This paper cites 2023 , eprint=.

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones 2023 , eprint=

Reference 67

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source=arxiv_source observed=2026-08-01T18:06:56.342744Z digest=sha256:1640da2169f9a6af3e8c363a2d12a04f1770e8b4721f1290fcc244ee31d1a2c5

Pith citing papers

No inbound Pith citation observations are available.