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Paper Citation Record · LEDGER

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders

As of 5 August 2026, this Paper Citation Record lists 100 of 182 outbound references and 0 inbound Pith citation observations for arXiv:2606.00746.

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

pith.paper-citation-record.v1
2606.00746 v1

Coverage vector

measured 100 of 182 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T19:23:08.100056Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

100 of 182 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved74
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch23

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e4b188c5-09e5-429c-8371-2a6746ad4450 · outbound

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

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 1

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local_arxiv, observed 2026-06-28T19:32:35.279215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:84c3379cfabe4909d6837c5a7fe199721000dd7f059a0c68c4755aac04b3a478

Observation b4d011d1-a7ba-4a29-b7b1-40c79b015e96 · outbound

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

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 2

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metadata mismatch
local_arxiv, observed 2026-06-28T19:32:35.263227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:3623c362f94b3f0214fbf97da610133ce8f6bde1a516fe14efed7d687cd4e289

Observation 888d9522-7cba-4a30-87f8-102e12af1643 · outbound

This paper cites VMamba: Visual State Space Model.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders VMamba: Visual State Space Model

Reference 3

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local_arxiv, observed 2026-06-28T19:32:35.260657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:82b60c0198619670fcc57101de3454ad9cead6b701644c40a4b31d8f1ce340a1

Observation 5dba8a17-4535-4c7f-8b2a-5b13cc47ab47 · outbound

This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 4

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arxiv_id, observed 2026-06-28T19:32:35.297416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:806d486dbc089af153bd9dbc5ebf1c28770396e9237da6f27b4d64a4dbbe83ae

Observation 45583f00-39d9-4f43-9eba-06ad05ccc93c · outbound

This paper cites Understanding Robustness of Visual State Space Models for Image Classification.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Understanding Robustness of Visual State Space Models for Image Classification

Reference 5

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verified exact
arxiv_id, observed 2026-06-28T19:32:35.276576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:ca4f35018b3d75d37d31f76e9c11c0fdf7dd45dc336ca82628f9c8c4294cd284

Observation 5dba864d-40c4-4e1d-bdb0-a09366f264cb · outbound

This paper cites DAT++: Spatially Dynamic Vision Transformer with Deformable Attention.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders DAT++: Spatially Dynamic Vision Transformer with Deformable Attention

Reference 6

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verified exact
arxiv_id, observed 2026-06-28T19:32:35.308210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:60bdc2413be13747ae12abad5a33072618c0d6f7b24931e672a4475ae368ecbc

Observation ba4d62de-9062-46cf-aaa1-05d554342640 · outbound

This paper cites CVPR , pages=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , pages=

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:484023b0e067b19c658fbe966ec4161de2fcdbcb6ad125db17445150c12a685c

Observation 43b30545-5901-48e3-b3dc-6e576e27104b · outbound

This paper cites RepViT: Revisiting Mobile CNN From ViT Perspective.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders RepViT: Revisiting Mobile CNN From ViT Perspective

Reference 8

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verified exact
arxiv_id, observed 2026-06-28T19:32:35.281790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:321cd99ae22b38d8d78e636edc8896cea90b24499979b6fb09a534224a28e704

Observation d24f9ed9-27fc-4a6b-9ab4-f1c0503cc70b · outbound

This paper cites EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba

Reference 9

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:35.299964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:cc581953bc061a064df8dadd8a467e269dd6fb1ace66d6cf5b4cc9709c92a857

Observation 40ae98a0-3177-4651-b05b-b6af5617ab17 · outbound

This paper cites ICML , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders ICML , year=

Reference 10

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:202ac642cde9d3e83424e6a201b22bcefb787d5631b7161db88ed7b6964ef8c9

Observation 300942f0-8e60-4b9e-9220-df8ca3f950fb · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 11

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:b1c06e7255d8d090e41ca1e171dced8dfdf4c2d0336a1452849d1ce74b18f542

Observation b4fbe354-483c-4353-838a-797d131925bb · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 12

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:3c8906efe16fffb36bfe76564e97dd327d0c94d3f210bf83d99b1c98433b5c5b

Observation d30290c1-0067-4987-909e-4f5d463323ce · outbound

This paper cites NeurIPS , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders NeurIPS , year=

Reference 13

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:8178686ab7182260498e07482f3eb201cd61daf1352c09162d59b3ed1666f847

Observation c2925a96-6516-4e55-b547-cddd5fcd01fd · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 14

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metadata mismatch
local_arxiv, observed 2026-06-28T19:32:35.284479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:b39d609378c53f5f3a29e40af4c5ddc23ce8aade9a8a3ef5167eb4020111723d

Observation 0e17c982-75c7-4e8f-81f9-9109c71b2eb5 · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 15

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Source-reported events for the cited work

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:97553a481077358235e8da08d2b04e33cd0660f5c655aedf09fefe59557a199a

Observation 52ae0eab-ea18-4958-8a6e-434215f9d483 · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 16

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:26653b2df98ba76f75e9ffd35eeb127f0b28c05c964e29f7957615dabc90ffb1

Observation 3d27b3dc-2ce3-4203-ab48-be9785ff7a8c · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 17

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no resolver link, observed 2026-06-28T19:23:08.100056Z

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:3a0ff62474616be86fc6ceda81ed0ccd2f99c7045621b0a4ff41a3fdab69f67f

Observation 2b2d09a3-9f8d-4683-ab67-8ebc972fe9ac · outbound

This paper cites CVPR , year =.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year =

Reference 18

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unresolved
no resolver link, observed 2026-06-28T19:23:08.100056Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:09d41712a92201c3da176beda6e89ba9aef5b87f57e8b69384f5b9c85cd30c52

Observation 835e3033-de84-4acd-802c-e72175b8ff28 · outbound

This paper cites ECCV , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders ECCV , year=

Reference 19

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no resolver link, observed 2026-06-28T19:23:08.100056Z

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:08bdb034e4cf08093e4e0f594dd8e1ab133436ff8f5a88606f634eb345e4fd8e

Observation 5f4c3784-9ee8-4700-8f04-5fbd707f0210 · outbound

This paper cites Computational Visual Media , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Computational Visual Media , year=

Reference 20

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:dc0500be3bb8b275481c2927673a740089dc8f85c89b4c276b40063a27640884

Observation 935bb5ca-85d3-4254-91ba-6369a822f059 · outbound

This paper cites NeurIPS , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders NeurIPS , year=

Reference 21

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:fd00ae32c46fc7c8b6c35985467eae104065650538a35259cc05c4cce2c7e0d7

Observation 90a49eea-1d81-4e70-a93a-147b74283d38 · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 22

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:d6bf56d4738bf0823ae30c7dfeee008a666af59a2206f4cbdbcc8ecc893a92bb

Observation c244fcb3-b250-45bd-a86b-88cc8c785756 · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 23

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:992c84dcd95791746992da2b528821d2b3296b9536e79b07991b898f92a00c73

Observation d9eb9fd4-2d68-441f-888d-875ccd5d4365 · outbound

This paper cites ECCV , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders ECCV , year=

Reference 24

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no resolver link, observed 2026-06-28T19:23:08.100056Z

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:3dc09f77cc80642d729265a08d04ad99cfe50767e729e3b0b3838aa8743f2eb4

Observation 082ec11b-6b95-4633-b883-3f639f964c26 · outbound

This paper cites ICLR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders ICLR , year=

Reference 25

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:2198024db834aed2f1e317a76aefc1a13af48ee85ca8872cc573f74c2f48f6cb

Observation 4650d050-7f97-49cb-8684-a7df64c79473 · outbound

This paper cites Layer Normalization.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Layer Normalization

Reference 26

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metadata mismatch
local_arxiv, observed 2026-06-28T19:32:35.292139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6d75b844ada2cad27d31da2a656cd472df551e439dd9cc43a99eb8acd55c7291

Observation de2e43bf-dc4b-4fa4-b36e-6ba7a57f400f · outbound

This paper cites ICCV , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders ICCV , year=

Reference 27

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6e635beb1bb3a758a1a23e33ff115a13254fdb590d81b84b60996dbc740c5783

Observation f29001cc-2afa-455e-934f-04043bf731ef · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 28

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:437409514b5bb61cd68c3f4b33494aa30e8fe81c50efabc13913dd99036bd01b

Observation fb6725ad-5a20-456a-9ed6-6942eca1d4bd · outbound

This paper cites NeurIPS , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders NeurIPS , year=

Reference 29

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unresolved
no resolver link, observed 2026-06-28T19:23:08.100056Z

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6cfc62c37f5f50dfe9746b9864f80482c15865b043877b4346a8d2155b1ec4fe

Observation 374b012a-5cf0-47f4-8869-cc7684176c1d · outbound

This paper cites InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions

Reference 30

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:35.287136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:49e4c5bee01fc45567a41f46c3fd23233e636e57840f2c8fc5b1c62e90e29092

Observation 3548c9a8-4a88-48aa-8272-43797c5f2c59 · outbound

This paper cites PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition

Reference 31

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:35.268642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:802f4279cda06f977d14278176b70f4f454b6efcc64a5f7ecd14524a7ed35acc

Observation 71d2a1b3-7717-442c-aa74-db3b007138fc · outbound

This paper cites NeurIPS , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders NeurIPS , year=

Reference 32

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:1e370bec009946f31fb2a4fea8709372ace4f78e7e71ceacaa4232a7ac4a0368

Observation ed0cc818-8fa0-4035-8176-2325a940742c · outbound

This paper cites MambaOut: Do We Really Need Mamba for Vision?.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders MambaOut: Do We Really Need Mamba for Vision?

Reference 33

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:35.271250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:70d66f790343daba3d45c4f5ecd7a293f54f90af1bb3790aa8d0415b8e0b7f28

Observation 68958883-4721-45a6-9786-155667022fa0 · outbound

This paper cites Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model

Reference 34

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:35.311195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:d6fcd32b2a45187fd7fa3e9faef11f3011ebc9a72589623a7015c4b0ed30e276

Observation 70868a02-ccef-4368-b097-4fda070509ac · outbound

This paper cites cosFormer: Rethinking Softmax in Attention.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders cosFormer: Rethinking Softmax in Attention

Reference 35

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:35.302922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:8c1286450427b0188f6a646dca378f740de4adac9d254bfc83ed7c60480f5612

Observation 93eacc54-4d0b-4985-b686-5ac6542ee0ad · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 36

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metadata mismatch
local_arxiv, observed 2026-06-28T19:32:35.273774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6c4b779a489a7d7693290b16a6cdd868b324bb387d3c71f1055bfc3c77fdf99b

Observation b126738a-5202-4ecd-afcf-e96f5a9d7349 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 37

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:f7f8124c9a99014f8f65836cb0801a190db80160ab83ce0f2afaa83412a5fc96

Observation 42f70386-c984-4c3b-9966-252e65892a67 · outbound

This paper cites ECCV , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders ECCV , year=

Reference 38

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:eb18632fe05fd51a8e7e1e42709ffee05c9661024abfe979484c3c832d758422

Observation aebcabb0-97d7-4709-9921-996e36a7077f · outbound

This paper cites WACV , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders WACV , year=

Reference 39

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:04b9d2a41cf56d6273fad6c46b3131a90c2a6bfd1a5f763aceab8a666ff6c5d6

Observation 17b70152-6aef-41eb-97b4-bbd621650237 · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 40

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:182df580f19ecdc408165cc3faa31ce4fac47ee4d209293163e6b4df23dbfcce

Observation 17201662-0422-4c37-a156-d9e68d3f3b33 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 41

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local_arxiv, observed 2026-06-28T19:32:35.265687Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:843ab173d31418d2f0e3cd24a4c02b48b4dd10f286e972d68aa9f1029574d422

Observation ef04d28f-25ba-480f-858e-377fed441a6e · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 42

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:89ec9ce664de04dd942606eb470fb0b5c48f252f8edadebe83a8aab8f785886e

Observation ffabd472-8a7d-4c2f-812a-093ae59bd82a · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:41d706e0890b5909cb5798657708fba8c0c315179f6bfe54543f2aa12ab0b845

Observation dba2d969-7de9-48e2-8546-eb5999ecf15a · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 44

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:f71310c3a72a445708f164e72599b0c6d70bfc407009efde2d7c1debc44d39b4

Observation 6c0074bb-8ea8-462f-9f0e-2978cf7af824 · outbound

This paper cites and Feng, Jiashi and Yan, Shuicheng , title =.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders and Feng, Jiashi and Yan, Shuicheng , title =

Reference 45

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:db4567996bb42f44d9bb46b1a9ef81d2980eaa6a050b5c2d68d1693dc21a421a

Observation a3f6a163-b95c-4d36-be82-64c826c484df · outbound

This paper cites CVPR , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year=

Reference 46

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6b13a6691a740537fe9e120b843f1feb71c98a4d8bfc6baed195958d463844ed

Observation f39e1696-b515-4c4a-98bf-a1a62ae49edf · outbound

This paper cites 2024 , journal =.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders 2024 , journal =

Reference 47

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:0c6e029de6aadb234d932ef483a78144294d7eb33b6c1fe0d887a1fd26f8faeb

Observation de34549c-7e59-43dc-ac8e-24150d688ea3 · outbound

This paper cites 2024 , journal =.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders 2024 , journal =

Reference 48

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:f3bc348b2f43d48d3b3ef400f695d01cc6ea8f012ee72cc9619f3ea1624ad8b2

Observation 87c96125-8ae2-44e5-b8a7-5e9608de4bff · outbound

This paper cites 2024 , journal =.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders 2024 , journal =

Reference 49

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:e3c8af40b519645cedae077d6625b600861ab4eb86e451d3f49b6ce863aa2d07

Observation 35625092-286b-4f70-91f7-5caea85557dd · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 50

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:f2da7ee77fa525c39709eb550705e2dbf8468aec6bbc7a46e4f75e573ab3f6b7

Observation 27d67e74-ae40-483b-a437-4a17a93145e2 · outbound

This paper cites 2024 , journal =.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders 2024 , journal =

Reference 51

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:cec529a51e8c7b4a3351b9a2db708c6349681c152c734491b69642af0121feb5

Observation 8289bc1e-7087-4bb8-83ae-0ad241b159e0 · outbound

This paper cites Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data

Reference 52

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arxiv_id, observed 2026-06-28T19:32:35.289726Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:a6f1921ec1ec3801f63991bda6e9d3db43b90092c73765ca4320fc64f288b688

Observation 8e54701d-9ded-4bcb-bed1-3a24f1271c86 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 53

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:2380ca3fd693b860a634586388726b08c5ce42742057435d4efffb670a3c8b15

Observation 2c0f9cbf-c362-4b18-a234-caeb4dc91e84 · outbound

This paper cites Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 54

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arxiv_id, observed 2026-06-28T19:32:35.294812Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:5db09f6ba98e97dcc98f37df4aae236b73413496e001852975a8743af5bcc7c8

Observation 7b440d95-3035-49fb-9f90-3164a7080b54 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:ef89532f4b05a685893e93a82df51b21e00ed858492168ed7a0115ed1a9622c7

Observation 28ab6607-0886-4290-b90f-81c9e052d87c · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 56

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:9aa1d7d6a4de193bc3032c1ff9297ba846047a9ae213efeaf4ede29adbe6cc99

Observation 25af2553-0d47-45bc-ba18-4fb93a4b2dd1 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 57

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:49a3ead4e4e7c1c38bf8685d2fde8823ab1be31f816576194022b034cc991eb5

Observation 50d0114e-4b49-4c7a-bc3e-df4367295174 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 58

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:add3aaa312846a87204020802c2579ea2168a16a0f32b6970ed493c847a4fc7f

Observation a092b124-546a-452c-96ef-39600868fe35 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 59

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:f864abecd29ee6cd8d6a5142d825f5a32b0a648b34cd70b62dbe8d68f5e6872e

Observation bb1e4fa8-323b-4738-9464-6c22b22df1d1 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Generating Long Sequences with Sparse Transformers

Reference 60

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local_arxiv, observed 2026-06-28T19:32:35.305623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:19d3c9b85873571019276600fa3ee0e82607a31523502ba1d4a68c423b0b7520

Observation 4c6e72d7-382e-41de-8dd1-d6ea9b684ec3 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 61

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:ff1f16f1b668b0e9cb0e81e30d8dd4f5d5f91edf8807a7f388ded672ffe733e9

Observation ca81ba48-8964-44cd-bff2-ad8313931b65 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 62

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:cabf96951ac2fcb8bac257da5a57270684859a001f1a6caf8dd7176ddcd50c50

Observation 464ead9d-8216-41f2-977b-93e4d6626045 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 63

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:9ee0e248a3aa4c0acdd62d7227e0b2002e36c9bae2f7d51d4734d3a327c6b7f5

Observation a3c4f514-cd98-49fe-aff4-ce4ecf895ed7 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 64

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:537fc0f5b88186399ea63918e993149990cef14b45040471d6b09b18d91e5140

Observation bb4a0908-68c6-41a3-a8da-1a6e3bc2a06c · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 65

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:007972e49e9bebc813f7a64bd1d77956010fd183cca917c3d2770c1ebd5413bf

Observation 7f76922e-36a2-46fe-b215-9e14cc781c8f · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 66

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:3befd1cd2c4701968e30edd88fb2fd918a68c5d36c6b6facac227ae1f3ae0dfc

Observation 5521705a-b75b-4804-8ae9-88845100713d · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 67

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:1c33be2a8003e5b21fa2f2fa1f9f8b19055a3090510d5ec6b614d4002f6e765f

Observation a176950d-2cc1-483c-ba04-561fde4cf394 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Linformer: Self-Attention with Linear Complexity

Reference 68

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metadata mismatch
local_arxiv, observed 2026-06-28T19:32:35.204429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:10da806c9e08d251169c2cfb4b89e9c25796e4c8d31140f18c53e1f8d596a47b

Observation 0f6a2a58-5f54-4a32-a56f-b580e732c8dd · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 69

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:0803fc69dea11fef06de5db24295bcfb32639bc153ee8edabe339f6b20727e23

Observation d68b5971-8595-49a9-beca-35dc6e601772 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 70

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6bb5cc1ac1c9c27be63069f413dbc677089c36a36528f04b163d5637c95d08f5

Observation 22f79374-f787-4c6e-a2b8-8aea3df1a721 · outbound

This paper cites Neurocomputing , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Neurocomputing , year=

Reference 71

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:68a49820876016267f442c27ec0249806289f56804bd26144b0b16c57965ec0f

Observation bf71b658-ccbd-4693-947e-ef9d79170588 · outbound

This paper cites EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision

Reference 72

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arxiv_id, observed 2026-06-28T19:32:35.236523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6a68a8b7f3658f5df5fc060d609f9449c6f78f852c755512b71a3b1f5f9490b1

Observation a20da377-a776-4ee3-82ab-f7c88743fbfd · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 73

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:86d23791ca437d77f2ffc1e0f701d5f7dfc2dd3f03c48fe05644e6ecedb892ec

Observation c1c4718e-97ad-4e51-b965-57651116589d · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 74

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:d86fdd658c92bc778639e344372648c22604d3998b5b9a3d87e62e3a663cb678

Observation 9f5c0bac-b784-4d77-a56c-938367a30acf · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 75

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metadata mismatch
local_arxiv, observed 2026-06-28T19:32:35.207389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:0397d39d896dfdfb659c135cbe83e51ba46203840fc956e983567195da31bf12

Observation 89d8c85e-7f53-494e-aef4-5df02432faa6 · outbound

This paper cites ACM SIGGRAPH 2022 conference proceedings , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders ACM SIGGRAPH 2022 conference proceedings , year=

Reference 76

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:19dbfa450d2049664f2b865425ac00b5b67f73cddae2151be28244b6c31f3aec

Observation 4248b66c-3883-4174-9e5c-72f50aa71177 · outbound

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

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Advances in neural information processing systems , year=

Reference 77

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:58c65e1c7150a13aa9f94c65a28b2c77867e931825bf618306a8a95cdef69fa9

Observation 629253f5-6f75-448f-9dd8-743591747d4d · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 78

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:bb11c86809e11fa1d310e5413c6c167b286a2d29605207bc60d525d8a5e2dad5

Observation 7cb0aaf4-0146-4d95-904a-037bd07127a8 · outbound

This paper cites Thirty-seventh Conference on Neural Information Processing Systems , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Thirty-seventh Conference on Neural Information Processing Systems , year=

Reference 79

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:cef0ee2a26a04287c6d0ef4cd29ff61196f220c6f64f2ba4d2464140353ff007

Observation 18c79bc6-fd01-41e1-ba39-3f9ee4376531 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=

Reference 80

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:8cda0b3f6fb3d74763802dfe7aefac0579292ce43cb088beb8abbdbed1b36a58

Observation 69c2d5d5-5111-443f-8616-44b61d1ce32a · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 81

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Observation 2f0d0ee5-3b6c-4f73-afb2-d60266e5c664 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 82

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Observation f3830a27-a2aa-42f5-83f0-dc27eb208764 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 83

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:18e53ad761ad4d090c976880e1f9d1e372179286902dcb0a552f64f278f51d1b

Observation 91f17848-bc7b-4678-bb7e-f67ede76d2c6 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 84

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Observation f50d8dd8-734f-4642-9c44-11998971d5ab · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 85

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:479b4c11bd3221ed72f520355dab23bb3965f88250b8f984ccf0f8ff32158b02

Observation 89947199-e67e-4188-8e02-628b3c471891 · outbound

This paper cites Generating Images with Sparse Representations.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Generating Images with Sparse Representations

Reference 86

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arxiv_id, observed 2026-06-28T19:32:35.215164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 54ef86a3-a324-4c51-aaab-2eb983f80a76 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 87

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Observation 71147a4d-554d-49c4-9a14-36482330852e · outbound

This paper cites Decoupled Weight Decay Regularization.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Decoupled Weight Decay Regularization

Reference 88

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metadata mismatch
local_arxiv, observed 2026-06-28T19:32:35.201951Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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Observation 535d8e79-3b45-4fd1-9449-d2dc2140227d · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 89

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:358b3e6e655e9863d94c39279a0581f1124491188e653799b0d260f9897f0f7b

Observation 5bb397b9-69dd-4843-9b99-df24ee2786cf · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 90

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:4ee7b1b6d124635f8e8ebf956195df3a6d33f2f604ebafbefb87f9bdea0c939f

Observation c1eb66ac-bb9a-4fb9-bb52-8d3eae5b8bf3 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 91

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:47c1b35fc108553a6df08aa32b3f6d3f29f52905abf40cf146110075edcedf39

Observation 333895bc-9348-497e-bf2a-7fd1cb9bdb12 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 92

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:bd606de66028d5b6e16ce1d724d31fc064144372e99cab9ceada12c293d3d6d1

Observation dc68aadd-8da5-427c-ba51-90623f87ebd8 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 93

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:56155130e210aaa8f457916cecd79153541f4da1160d11ddb6c1ff3fdcd95081

Observation 566340ec-ba00-4ad5-8371-3c2852582c13 · outbound

This paper cites 2-D SSM: A General Spatial Layer for Visual Transformers.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders 2-D SSM: A General Spatial Layer for Visual Transformers

Reference 94

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metadata mismatch
arxiv_id, observed 2026-06-28T19:32:35.217870Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:ad00261d4240e7fd97b92cd14c6ab4835ef98887cfacafb6fbb39017f0d84840

Observation 59b611c4-1d54-4c76-bd6c-98ec4a5901d9 · outbound

This paper cites and Ermon, Stefano and Rudra, Atri and R.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders and Ermon, Stefano and Rudra, Atri and R

Reference 95

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:fed3ea76cc56a6e1598123b230fcb53d34d91aeb7d2905f83d8c4236356286b5

Observation 3cbdcdf3-b3c5-4ec3-abbf-18cb65df655a · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 96

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:9a5539f7927baf99a0cdd54f55b694902539965dab054166d6fb94209b9fc93f

Observation 5b250ba5-2671-4fe4-939e-c99577070ff1 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 97

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:8e7fd469698b4b6172a389dcf68b8acd313e0bfc8b3b9351d09f49cd82dd8807

Observation d1d22a2d-e22d-4d69-b280-0be66d5b8c5d · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 98

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:f94cecfe33a44850c1483137fcdc70be89d2e9bb08e518a9a2165265e6e0c079

Observation 0546a1a4-d3f9-4e06-88af-a41ed99613b0 · outbound

This paper cites CVPR , year =.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders CVPR , year =

Reference 99

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:8e3746c84a1235f029e2b66ca6d2a991778a2a2aee916ab416efe3bea10dd5b2

Observation 4ac70d08-89e4-4fe5-bff7-2fb62d2dd413 · outbound

This paper cites an unresolved cited work.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Unresolved cited work

Reference 100

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source=arxiv_source observed=2026-06-28T19:23:08.100056Z digest=sha256:6d0d7acb416331b8da09a130b00ba984a014047699a076ed1f1ce573f718498f

Pith citing papers

No inbound Pith citation observations are available.