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Paper Citation Record · LEDGER

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding

As of 9 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2506.10084.

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

pith.paper-citation-record.v1
2506.10084 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:39:11.182787Z

measured 60 of 60 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.

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

60 of 60 outbound references displayed

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  • verified fuzzy28
  • unresolved27
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 806d8e47-d208-41e3-b838-7bb9ca2bad95 · outbound

This paper cites Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution,

Reference 1

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Observation a9794475-dbfe-4b0d-8dd0-006ce61849c8 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Xception: Deep learning with depthwise separable convolutions,

Reference 2

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Observation 0ea55165-17df-42bf-a97a-dbd5c9311287 · outbound

This paper cites Tackling multipath and biased training data for imu-assisted ble proximity detection,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Tackling multipath and biased training data for imu-assisted ble proximity detection,

Reference 3

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Observation c49b8fdb-2678-4aad-99a5-bdc35f3f7ca6 · outbound

This paper cites Micronet: Improving image recognition with extremely low flops,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Micronet: Improving image recognition with extremely low flops,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e8c588ec-9bef-4146-980e-6821661967db · outbound

This paper cites Rigid-Motion Scattering for Texture Classification.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Rigid-Motion Scattering for Texture Classification

Reference 5

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verified exact
local_arxiv, observed 2026-08-07T04:39:12.702802Z

Source-reported events for the cited work

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Observation 3f20b818-ae39-48ef-84ed-c9f02be9b078 · outbound

This paper cites Hetconv: Heterogeneous kernel-based convolutions for deep cnns,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Hetconv: Heterogeneous kernel-based convolutions for deep cnns,

Reference 6

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Source-reported events for the cited work

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Observation d74de223-2c67-4adb-9d3a-49a4af2cd77d · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation d48247c5-4d8f-4b28-bf79-41002e3eaf69 · outbound

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

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Training data-efficient image transformers & distillation through attention,

Reference 8

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Source-reported events for the cited work

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Observation 53181714-b549-4a03-b34e-01e58c5f5866 · outbound

This paper cites A depth-first search-based algorithm for the minimization of the largest connected component in networks,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding A depth-first search-based algorithm for the minimization of the largest connected component in networks,

Reference 9

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Source-reported events for the cited work

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Observation 363480b2-1913-4e35-87fe-d33a344f4322 · outbound

This paper cites Interleaved group convolutions,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Interleaved group convolutions,

Reference 10

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation a32bc01e-f695-4ae1-8013-67f02ca96c2d · outbound

This paper cites Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services

Reference 11

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verified exact
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Source-reported events for the cited work

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Observation 204a240c-1d3d-467e-b0c2-7a0cb5288928 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Imagenet classification with deep convolutional neural networks,

Reference 12

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Observation ab41c3a3-f979-4411-81bb-cff459424df4 · outbound

This paper cites Searching for mobilenetv3,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Searching for mobilenetv3,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 8898e184-204c-4b79-a254-a54fe6980702 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 14

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Observation 105c30a2-3fc9-4a92-b74a-a17f173e3e1c · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 15

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Observation 8a7718d3-4ee5-4016-b8b5-e0c741e0f250 · outbound

This paper cites Shufflenet v2: Practical guidelines for efficient cnn architecture design,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Shufflenet v2: Practical guidelines for efficient cnn architecture design,

Reference 16

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Observation 7a2a274a-5149-4f3a-8051-ae92b0c3f617 · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural network for mobile devices,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Shufflenet: An extremely efficient convolutional neural network for mobile devices,

Reference 17

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 96829a3a-f280-45ea-8857-b9fea6ad9375 · outbound

This paper cites Ghostnet: More features from cheap operations,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Ghostnet: More features from cheap operations,

Reference 18

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Source-reported events for the cited work

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Observation e65c9664-4b9b-4f58-aa9f-e75110421d9b · outbound

This paper cites Deep residual learning for image recognition,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Deep residual learning for image recognition,

Reference 19

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Observation ca73c324-7d39-4e9c-ad1d-c045698fe329 · outbound

This paper cites Condensenet: An efficient densenet using learned group convolutions,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Condensenet: An efficient densenet using learned group convolutions,

Reference 20

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Source-reported events for the cited work

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Observation 1244ce0f-29ce-411d-b220-514fca735249 · outbound

This paper cites Efficientnetv2: Smaller models and faster training,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Efficientnetv2: Smaller models and faster training,

Reference 21

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Observation 29746ca6-251a-4b66-bff0-cba44372ed10 · outbound

This paper cites Run, don’t walk: Chasing higher FLOPS for faster neural networks,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Run, don’t walk: Chasing higher FLOPS for faster neural networks,

Reference 22

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Observation 476f1628-b234-4ec6-a41b-08afb167e9ad · outbound

This paper cites Rewrite the stars,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Rewrite the stars,

Reference 23

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Source-reported events for the cited work

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Observation de7238f1-fe82-4a54-812b-b7a23b190241 · outbound

This paper cites Attention is all you need,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Attention is all you need,

Reference 24

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Observation 578007ab-ceb0-4bd9-9ffc-b962d17532de · outbound

This paper cites Social ode: Multi-agent trajectory forecasting with neural ordinary differential equations,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Social ode: Multi-agent trajectory forecasting with neural ordinary differential equations,

Reference 25

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Observation f3e75e37-b0af-4a03-8caf-d19a2825939c · outbound

This paper cites How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 26

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Observation 70dc64a6-ebba-4301-8da2-f96756616281 · outbound

This paper cites DeiT III: Revenge of the ViT.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding DeiT III: Revenge of the ViT

Reference 27

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Observation a7facae7-7179-412d-aaf1-193aac658bdf · outbound

This paper cites Levit: a vision transformer in convnet’s clothing for faster inference,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Levit: a vision transformer in convnet’s clothing for faster inference,

Reference 28

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Source-reported events for the cited work

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Observation 0dadef36-4131-4e45-93f6-d65774d4a2ce · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Swin transformer v2: Scaling up capacity and resolution,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 8666fb80-fab3-4fb9-95e0-50a9932de344 · outbound

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

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 30

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Source-reported events for the cited work

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Observation a228e0e5-ee26-42a9-862b-d9f8f75c78ff · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,

Reference 31

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Observation 33c6d456-774f-4274-a9f7-2039309d2d0a · outbound

This paper cites A tree-based structure-aware transformer decoder for image-to-markup generation,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding A tree-based structure-aware transformer decoder for image-to-markup generation,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 1ec2a9c0-e65c-4fbd-9b49-49701206e506 · outbound

This paper cites Xcit: Cross-covariance image transformers,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Xcit: Cross-covariance image transformers,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation cb63ece0-2404-4a42-b679-73dab271b776 · outbound

This paper cites LightViT: Towards Light-Weight Convolution-Free Vision Transformers.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding LightViT: Towards Light-Weight Convolution-Free Vision Transformers

Reference 34

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Observation 5a2878f0-a64b-490f-b219-3f0d79c59a30 · outbound

This paper cites Soft: Softmax-free transformer with linear complexity,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Soft: Softmax-free transformer with linear complexity,

Reference 35

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raw_fallback, observed 2026-08-07T04:39:15.344339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation fc1c2a2c-2a17-405a-942f-831a1d2fccc5 · outbound

This paper cites QuadTree Attention for Vision Transformers.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding QuadTree Attention for Vision Transformers

Reference 36

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source=pdf_text observed=2026-08-07T04:39:08.126990Z digest=sha256:eeaadc70aeedb32ac04f39a846d7aefbb80faa0a3ae4159e81dace27b6ad40d6

Observation 16d68f75-f183-4c73-8c1e-292a2c3492e2 · outbound

This paper cites Scaling local self-attention for parameter efficient visual backbones,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Scaling local self-attention for parameter efficient visual backbones,

Reference 37

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raw_fallback, observed 2026-08-07T04:39:15.036834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:08.296312Z digest=sha256:335c264cb98b28913ff0ee67cd728d432b041a1cdc2bc4c3e45f8f7834b3cfd8

Observation b89320f5-7f1e-4547-bc20-fb7afe4f1696 · outbound

This paper cites Mobileformer: Bridging mobilenet and transformer,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Mobileformer: Bridging mobilenet and transformer,

Reference 38

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raw_fallback, observed 2026-08-07T04:39:14.797926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:08.432636Z digest=sha256:a9a5d245ef683fc93735dd901435facb215ecc62be1006d317bdc4a01aa8c5cd

Observation 2e7ebdf1-c6a2-4aa4-a185-48e40591ec7b · outbound

This paper cites Coatnet: Marrying convolution and attention for all data sizes,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Coatnet: Marrying convolution and attention for all data sizes,

Reference 39

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raw_fallback, observed 2026-08-07T04:39:14.536225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:08.522913Z digest=sha256:13d503247b5fd7ce7843505c3b4707fd422eec1debdeb359617640d18486dc32

Observation e5547e08-0683-437c-828e-0527a945c6b6 · outbound

This paper cites Bottleneck transformers for visual recognition,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Bottleneck transformers for visual recognition,

Reference 40

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source=pdf_text observed=2026-08-07T04:39:08.626938Z digest=sha256:39a634124e57ce61a09b20f8bcde36dbf78a5f09e3871c2931937def13f107e0

Observation da2c2845-d02a-4876-9e7f-fd817e9bc5c3 · outbound

This paper cites EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

Reference 41

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source=pdf_text observed=2026-08-07T04:39:08.739752Z digest=sha256:f11b031c604736f8edf6505e2d1e5f78858054dbc72c9f8198a3261714d9b925

Observation b8be29fa-054c-4bb4-a88e-81d400880e4f · outbound

This paper cites EfficientFormer: Vision Transformers at MobileNet Speed.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding EfficientFormer: Vision Transformers at MobileNet Speed

Reference 42

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source=pdf_text observed=2026-08-07T04:39:08.867481Z digest=sha256:666368045060a2a037ba0256fd5d39f4a63a6bf0fc66ae0055807a44b77b6291

Observation add36f08-bf81-4b26-85e5-b8f7e260b0b7 · outbound

This paper cites Separable Self-attention for Mobile Vision Transformers.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Separable Self-attention for Mobile Vision Transformers

Reference 43

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source=pdf_text observed=2026-08-07T04:39:08.980998Z digest=sha256:a749c6603cfa6c2c21055f00cd7cda5d481d217dcd577be733e25163920307ec

Observation 376b15ef-3148-4f0b-92f9-c386b30537cd · outbound

This paper cites EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers

Reference 44

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verified exact
local_arxiv, observed 2026-08-07T04:39:12.035090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:09.135551Z digest=sha256:f7b498f9ce8c359748565e59b8e6db867dd1156ecd48d5b37195c80fbf4c6799

Observation b1ac31eb-bbaf-4ebb-aa67-b84c6f814494 · outbound

This paper cites CycleMLP: A MLP-like Architecture for Dense Prediction.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding CycleMLP: A MLP-like Architecture for Dense Prediction

Reference 45

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source=pdf_text observed=2026-08-07T04:39:09.219446Z digest=sha256:e110d8dcafd1ea319386292ca3346445787372f985806e4da64df6f4e6790740

Observation a609053e-ee6f-4cda-b4a5-fb144a050478 · outbound

This paper cites AS-MLP: An Axial Shifted MLP Architecture for Vision.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding AS-MLP: An Axial Shifted MLP Architecture for Vision

Reference 46

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source=pdf_text observed=2026-08-07T04:39:09.346061Z digest=sha256:dd3f73fa417e542dfd4720200c9f27aecaf899a7c9fff87b168faa51bbb383f0

Observation b7a86f5c-95df-45cd-96c9-01d0b0ece1da · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Mlp-mixer: An all-mlp architecture for vision,

Reference 47

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raw_fallback, observed 2026-08-07T04:39:14.295030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:09.506311Z digest=sha256:4cd98df4012ea87b3b4a431ab75bbb4e52fb2b65864c5f2952aadfa879da4e92

Observation f93c8576-a6b9-4469-8479-78528cb24b49 · outbound

This paper cites Are we ready for a new paradigm shift? a survey on visual deep mlp,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Are we ready for a new paradigm shift? a survey on visual deep mlp,

Reference 48

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raw_fallback, observed 2026-08-07T04:39:13.978801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:09.634113Z digest=sha256:e5a7d4ec2f8ede52936c5b21c42b3f1c0fb7140055d4cba8d3c8280f1e438005

Observation 52d47feb-e90b-4ae6-8d71-53906c5e2188 · outbound

This paper cites A convnet for the 2020s,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding A convnet for the 2020s,

Reference 49

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:09.725049Z digest=sha256:7614c60c89b20d0c2c2cdc9364f755234e82d222f094c075a295f4347dd23f9b

Observation fcbec2ac-5a05-42ef-9ed6-7c4135910e8b · outbound

This paper cites When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism

Reference 50

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local_arxiv, observed 2026-08-07T04:39:11.790921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:09.883037Z digest=sha256:884fa8f511cfc44930f519cfb2cc2dbb8977973ae31f89de6be183b9a4de9100

Observation 5b19edc8-e875-4459-8f54-b46c22740a60 · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 51

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source=pdf_text observed=2026-08-07T04:39:09.982714Z digest=sha256:0e6abcdc7b69612f2cdfcd3ffaa499f762b0a4b2a9362a77d14275c9dfe54a13

Observation 7d194f86-138c-4d8c-9f07-e2ee74652a97 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Relational inductive biases, deep learning, and graph networks

Reference 52

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source=pdf_text observed=2026-08-07T04:39:10.066867Z digest=sha256:c14fca93744c2ab1e6afe0a39486ed02bcc31c1e9e2ee17b3068c9dd17a7a745

Observation d7332823-a5ae-4fd2-aff0-917958b6d56c · outbound

This paper cites Learning fast approximations of sparse coding,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Learning fast approximations of sparse coding,

Reference 53

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source=pdf_text observed=2026-08-07T04:39:10.191112Z digest=sha256:e13b2476c9d668f45d88fcf31b8bef6dbe111e5709e96a6b7fdbbd31c4b4f30e

Observation 3df61d0f-e5a0-42c4-9af4-9b42dfde6da3 · outbound

This paper cites Neural execution of graph algorithms,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Neural execution of graph algorithms,

Reference 54

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raw_fallback, observed 2026-08-07T04:39:13.448185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:10.333516Z digest=sha256:44b8c10e9ae2a8a7187e5a09838d393e93f108e8bc67c92e9b89a0ddce4e0e85

Observation 8d0792c5-bf80-4c48-812c-10801ea6dc1d · outbound

This paper cites Compositional attention networks for machine reasoning,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Compositional attention networks for machine reasoning,

Reference 55

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raw_fallback, observed 2026-08-07T04:39:13.150582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:10.466359Z digest=sha256:5b271978fc6303d12f20c6aea7e49cca4de22642aaf14eddb99f35ced79200e8

Observation 060a362c-f2e1-4569-bf24-048c8638cbc4 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 56

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source=pdf_text observed=2026-08-07T04:39:10.610375Z digest=sha256:2c2bd40ebc6bc04076adeac1f02579be013a82c29bf43af61ec2b7624645baa8

Observation c72e8be9-2561-451f-9ee7-788ae2250630 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 57

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source=pdf_text observed=2026-08-07T04:39:10.743589Z digest=sha256:3e6319264f1c6308c5de14b7afabc1bd5ea36a9fdc0dc521796e866046e70bec

Observation 40814b94-6e4e-47c3-97cf-b4f77b01b0ad · outbound

This paper cites This looks like that: deep learning for interpretable image recognition,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding This looks like that: deep learning for interpretable image recognition,

Reference 58

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raw_fallback, observed 2026-08-07T04:39:12.957783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:10.918519Z digest=sha256:6e95fef76341900a7df7366b12998b5d01a99479e1af022304f45c50f9e127e2

Observation dc03a394-0c50-4b91-bcaa-d7a4bb769658 · outbound

This paper cites On the Performance of the Depth First Search Algorithm in Supercritical Random Graphs.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding On the Performance of the Depth First Search Algorithm in Supercritical Random Graphs

Reference 59

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local_arxiv, observed 2026-08-07T04:39:11.451753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T04:39:11.035503Z digest=sha256:61f7df5e4e6f6d95c68b9913fbc1555ca734a11534068d04f65bc314dcc98ffa

Observation fa19aaf5-5305-47da-a9eb-90ae8ece1ce1 · outbound

This paper cites Wide residual networks,.

DeepTraverse: A Depth-First Search Inspired Network for Algorithmic Visual Understanding Wide residual networks,

Reference 60

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T04:39:11.182787Z digest=sha256:1f86ddb2f8a94aac68586f4a4b2618596eb2e585bcc6f5f8d07649014b02715e

Pith citing papers

No inbound Pith citation observations are available.