Paper Citation Record · LEDGER

Ultra Ethernet's Design Principles and Architectural Innovations

As of 17 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 3 inbound Pith citation observations for arXiv:2508.08906.

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

pith.paper-citation-record.v1
2508.08906 v1

Coverage vector

measured 89 of 89 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:34:14.886343Z

measured 92 of 92 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T05:41:45.840297Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

89 of 89 outbound references displayed

verified exact2
verified fuzzy55
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation aa8b4012-4424-4ec9-ac00-777259045ccb · outbound

This paper cites Learning represen- tations and generative models for 3d point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Learning represen- tations and generative models for 3d point clouds

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.407458Z

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Unavailable: canonical work link unavailable.

Observation 5e0a55fe-b1c2-42ac-a004-ebeffa04ce49 · outbound

This paper cites Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding.

Ultra Ethernet's Design Principles and Architectural Innovations Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.413343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b8491ae-03c7-4459-bf0b-8d5b25851d4f · outbound

This paper cites Zamir, Helen Jiang, Ioan- nis Brilakis, Martin Fischer, and Silvio Savarese.

Ultra Ethernet's Design Principles and Architectural Innovations Zamir, Helen Jiang, Ioan- nis Brilakis, Martin Fischer, and Silvio Savarese

Reference 3

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Observation c4cbb466-749d-46b0-9778-328cc28c4bbb · outbound

This paper cites Deep clustering for unsupervised learning 5 GTPoint-MAEMaskClu (Ours) Figure B.

Ultra Ethernet's Design Principles and Architectural Innovations Deep clustering for unsupervised learning 5 GTPoint-MAEMaskClu (Ours) Figure B

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.422628Z

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Observation e4a48ff7-03b1-4486-aa99-b8f262076f80 · outbound

This paper cites SL3D: Self-supervised-Self-labeled 3D Recognition.

Ultra Ethernet's Design Principles and Architectural Innovations SL3D: Self-supervised-Self-labeled 3D Recognition

Reference 5

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Source-reported events for the cited work

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Observation bf72bcaa-2846-4c45-ab0c-10e73e541a04 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Ultra Ethernet's Design Principles and Architectural Innovations ShapeNet: An Information-Rich 3D Model Repository

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.431927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 056efe99-1edc-4063-87aa-0a82f86191ac · outbound

This paper cites Pimae: Point cloud and image interactive masked autoencoders for 3d object detection.

Ultra Ethernet's Design Principles and Architectural Innovations Pimae: Point cloud and image interactive masked autoencoders for 3d object detection

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.436878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cb0c8df-5280-45ab-b94e-350a80e9eebb · outbound

This paper cites Pointgpt: Auto-regressively generative pre- training from point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Pointgpt: Auto-regressively generative pre- training from point clouds

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.441098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ed4e867-2356-47aa-bf89-30f13a098a30 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Ultra Ethernet's Design Principles and Architectural Innovations A simple framework for contrastive learning of visual representations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.445481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5841a378-a349-4688-94b0-c28a51ddee20 · outbound

This paper cites Exploring simple siamese rep- resentation learning.

Ultra Ethernet's Design Principles and Architectural Innovations Exploring simple siamese rep- resentation learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.449508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72fd6e08-9bd0-45c9-9ed2-4871f9e73797 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Ultra Ethernet's Design Principles and Architectural Innovations Sinkhorn distances: Lightspeed computation of optimal transport

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.453661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 280a5a7b-2c91-4824-a79f-4bdc5fd56cc3 · outbound

This paper cites Scannet: 6 Richly-annotated 3d reconstructions of indoor scenes.

Ultra Ethernet's Design Principles and Architectural Innovations Scannet: 6 Richly-annotated 3d reconstructions of indoor scenes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.425425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 06dab86b-9b15-4d2a-ae4f-2046674df926 · outbound

This paper cites Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?.

Ultra Ethernet's Design Principles and Architectural Innovations Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.464355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 267fa097-b9b7-4aca-8dca-2def9f660e75 · outbound

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

Ultra Ethernet's Design Principles and Architectural Innovations An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.413046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 71416d23-1cf1-43e3-a563-ad7add2a16d2 · outbound

This paper cites Self-supervised learning on 3d point clouds by learning dis- crete generative models.

Ultra Ethernet's Design Principles and Architectural Innovations Self-supervised learning on 3d point clouds by learning dis- crete generative models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.399746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 38b83976-d220-44b8-b632-f01012089cd4 · outbound

This paper cites Point transformer.

Ultra Ethernet's Design Principles and Architectural Innovations Point transformer

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.387639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2e181b49-570c-4322-89f3-6f0eeed04945 · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised learning.

Ultra Ethernet's Design Principles and Architectural Innovations Bootstrap your own latent: A new approach to self-supervised learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.371197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9275ccd8-ad75-455e-a21c-26434fd98fd1 · outbound

This paper cites Efficiently mod- eling long sequences with structured state spaces.

Ultra Ethernet's Design Principles and Architectural Innovations Efficiently mod- eling long sequences with structured state spaces

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.487291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94af232c-94cb-461d-b6d5-6e8658ba1dfd · outbound

This paper cites Adamw and super- convergence is now the fastest way to train neural nets.

Ultra Ethernet's Design Principles and Architectural Innovations Adamw and super- convergence is now the fastest way to train neural nets

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.347519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3f2370e3-fe93-4c23-b536-080c455e4777 · outbound

This paper cites Pct: Point cloud transformer.

Ultra Ethernet's Design Principles and Architectural Innovations Pct: Point cloud transformer

Reference 20

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Source-reported events for the cited work

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Observation 0ce40893-287f-4a58-b70d-56b91be5459d · outbound

This paper cites Joint-mae: 2d-3d joint masked autoen- coders for 3d point cloud pre-training.

Ultra Ethernet's Design Principles and Architectural Innovations Joint-mae: 2d-3d joint masked autoen- coders for 3d point cloud pre-training

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.322375Z

Source-reported events for the cited work

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Observation f8b9923e-121e-4255-89ef-e9a297831817 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Ultra Ethernet's Design Principles and Architectural Innovations Masked autoencoders are scalable vision learners

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.508793Z

Source-reported events for the cited work

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Observation 3883ca1e-1f9f-4dba-a32e-5d03a186c9c6 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Ultra Ethernet's Design Principles and Architectural Innovations Denoising dif- fusion probabilistic models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.514451Z

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Observation 08640486-2e6e-44c5-8b05-355f1d12e86a · outbound

This paper cites Ponder: Point cloud pre-training via neural rendering.

Ultra Ethernet's Design Principles and Architectural Innovations Ponder: Point cloud pre-training via neural rendering

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.284747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6709c39b-dd98-47c7-93c0-803fd6dc6238 · outbound

This paper cites Spatio-temporal self-supervised representation learning for 3d point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Spatio-temporal self-supervised representation learning for 3d point clouds

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.522945Z

Source-reported events for the cited work

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Observation 18a5f5d9-8bc4-4aeb-a855-4721b0ffed36 · outbound

This paper cites Free-form language-based robotic reasoning and grasping.

Ultra Ethernet's Design Principles and Architectural Innovations Free-form language-based robotic reasoning and grasping

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1b063c8-3a08-44aa-8b25-a25930316daf · outbound

This paper cites Self-supervised feature learning by cross-modality and cross-view corre- spondences.

Ultra Ethernet's Design Principles and Architectural Innovations Self-supervised feature learning by cross-modality and cross-view corre- spondences

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.258261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 466e4714-cfa8-47a8-9f54-df95620fb6fc · outbound

This paper cites Point Cloud GAN.

Ultra Ethernet's Design Principles and Architectural Innovations Point Cloud GAN

Reference 28

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Source-reported events for the cited work

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Observation 0d3fa5a8-7a67-4cc2-a2f0-77aa1b9e7b68 · outbound

This paper cites Pointcnn: Convolution on x- transformed points.

Ultra Ethernet's Design Principles and Architectural Innovations Pointcnn: Convolution on x- transformed points

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.243242Z

Source-reported events for the cited work

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Observation 1dc34c1e-d9b9-45ca-a30d-3a5ee5f157ac · outbound

This paper cites Scenesplat: Gaussian splatting-based scene understanding with vision-language pretraining.

Ultra Ethernet's Design Principles and Architectural Innovations Scenesplat: Gaussian splatting-based scene understanding with vision-language pretraining

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.227038Z

Source-reported events for the cited work

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Observation 548733af-7183-48ca-b6b5-57e1896ef1d3 · outbound

This paper cites Pvafn: Point-voxel at- tention fusion network with multi-pooling enhancing for 3d object detection.

Ultra Ethernet's Design Principles and Architectural Innovations Pvafn: Point-voxel at- tention fusion network with multi-pooling enhancing for 3d object detection

Reference 31

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Observation b9d5420a-ba92-4614-bdb0-34ed46938395 · outbound

This paper cites Pointmamba: A simple state space model for point cloud analysis.

Ultra Ethernet's Design Principles and Architectural Innovations Pointmamba: A simple state space model for point cloud analysis

Reference 32

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Source-reported events for the cited work

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Observation 3d492fe2-addf-4b04-a23a-3bd1b8d3aeb7 · outbound

This paper cites Spatio-temporal graph diffusion for text-driven human motion generation.

Ultra Ethernet's Design Principles and Architectural Innovations Spatio-temporal graph diffusion for text-driven human motion generation

Reference 33

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aaf17686-abbd-4936-955a-01782793b606 · outbound

This paper cites Point discriminative learning for data- efficient 3d point cloud analysis.

Ultra Ethernet's Design Principles and Architectural Innovations Point discriminative learning for data- efficient 3d point cloud analysis

Reference 34

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Source-reported events for the cited work

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Observation 5d59a99f-93c2-4a2d-a561-8d20ebb01e01 · outbound

This paper cites Masked discrimina- tion for self-supervised learning on point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Masked discrimina- tion for self-supervised learning on point clouds

Reference 35

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Source-reported events for the cited work

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Observation 79ba7613-f724-42ba-bb8b-35b997ab6a0b · outbound

This paper cites L2g auto-encoder: Under- standing point clouds by local-to-global reconstruction with hierarchical self-attention.

Ultra Ethernet's Design Principles and Architectural Innovations L2g auto-encoder: Under- standing point clouds by local-to-global reconstruction with hierarchical self-attention

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.126107Z

Source-reported events for the cited work

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Observation 61d4a20f-e03f-425a-b00f-9a3472bed7ad · outbound

This paper cites Densepoint: Learning densely contextual representation for efficient point cloud process- ing.

Ultra Ethernet's Design Principles and Architectural Innovations Densepoint: Learning densely contextual representation for efficient point cloud process- ing

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.107639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8d64d92d-519c-485c-aa42-b6bbdd4907d5 · outbound

This paper cites Relation-shape convolutional neural network for point cloud analysis.

Ultra Ethernet's Design Principles and Architectural Innovations Relation-shape convolutional neural network for point cloud analysis

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.086903Z

Source-reported events for the cited work

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Observation cf2b96b4-7e07-4b3e-b30c-6e46a34c793c · outbound

This paper cites Pointclustering: Unsupervised point cloud pre-training using transformation invariance in clustering.

Ultra Ethernet's Design Principles and Architectural Innovations Pointclustering: Unsupervised point cloud pre-training using transformation invariance in clustering

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.068157Z

Source-reported events for the cited work

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Observation 505dfc7f-1946-4858-ac33-f6cb5f7c1579 · outbound

This paper cites Scenesplat++: A large dataset and compre- hensive benchmark for language gaussian splatting.

Ultra Ethernet's Design Principles and Architectural Innovations Scenesplat++: A large dataset and compre- hensive benchmark for language gaussian splatting

Reference 40

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Source-reported events for the cited work

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Observation de11fec0-782b-483d-a30b-dab60c31c65c · outbound

This paper cites ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining.

Ultra Ethernet's Design Principles and Architectural Innovations ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.607002Z

Source-reported events for the cited work

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Observation 5fb7a1a4-5e8d-4f7d-b78c-ab62de9c4afc · outbound

This paper cites Data augmentation- free unsupervised learning for 3d point cloud understanding.

Ultra Ethernet's Design Principles and Architectural Innovations Data augmentation- free unsupervised learning for 3d point cloud understanding

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.051743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76910fea-053c-466f-8273-1cad482418c9 · outbound

This paper cites Unsupervised point cloud representation learning by clustering and neural ren- dering.

Ultra Ethernet's Design Principles and Architectural Innovations Unsupervised point cloud representation learning by clustering and neural ren- dering

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.033899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5abb7620-5d9e-420d-a649-a1b09772782e · outbound

This paper cites Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding.

Ultra Ethernet's Design Principles and Architectural Innovations Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:34:15.124646Z

Source-reported events for the cited work

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Observation 9a84eab8-c45c-46df-810e-2576c17dddd1 · outbound

This paper cites Scene- graphloc: Cross-modal coarse visual localization on 3d scene graphs.

Ultra Ethernet's Design Principles and Architectural Innovations Scene- graphloc: Cross-modal coarse visual localization on 3d scene graphs

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:16.016107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6aba1b26-ee50-4f4c-ba68-b088ca378127 · outbound

This paper cites An end- to-end transformer model for 3d object detection.

Ultra Ethernet's Design Principles and Architectural Innovations An end- to-end transformer model for 3d object detection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.996903Z

Source-reported events for the cited work

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Observation f4864abe-2688-4835-b517-3bc6a6b4a322 · outbound

This paper cites Masked autoencoders for point cloud self-supervised learning.

Ultra Ethernet's Design Principles and Architectural Innovations Masked autoencoders for point cloud self-supervised learning

Reference 47

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Source-reported events for the cited work

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Observation 95b0b6d4-8251-4157-959c-138064dfca75 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Ultra Ethernet's Design Principles and Architectural Innovations Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.961012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 972ed4dc-44e9-4d98-a0cd-5e57af63f95a · outbound

This paper cites Pointnet++: Deep hier- archical feature learning on point sets in a metric space.

Ultra Ethernet's Design Principles and Architectural Innovations Pointnet++: Deep hier- archical feature learning on point sets in a metric space

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.945848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51d6d485-d1be-4812-9f29-fde7d70a623d · outbound

This paper cites Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining.

Ultra Ethernet's Design Principles and Architectural Innovations Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.927967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca1863a9-0c33-4da9-b1ad-337174cea4fd · outbound

This paper cites Dense-resolution network for point cloud classification and segmentation.

Ultra Ethernet's Design Principles and Architectural Innovations Dense-resolution network for point cloud classification and segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.910949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 912d4439-2585-4d09-b83b-c7fb4b9183b1 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Ultra Ethernet's Design Principles and Architectural Innovations Learn- ing transferable visual models from natural language super- vision

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.660757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a781200-f473-452f-bb33-193c6b3b3949 · outbound

This paper cites Global-local bidi- rectional reasoning for unsupervised representation learning of 3d point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Global-local bidi- rectional reasoning for unsupervised representation learning of 3d point clouds

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.884007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 39619fef-0497-42be-aa5c-97fa36b96d7c · outbound

This paper cites Cascaded cross mlp- mixer gans for cross-view image translation.

Ultra Ethernet's Design Principles and Architectural Innovations Cascaded cross mlp- mixer gans for cross-view image translation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.868047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f941705f-d3bb-4bfd-8347-d12a34c67fd0 · outbound

This paper cites Masked jigsaw puzzle: A versatile po- sition embedding for vision transformers.

Ultra Ethernet's Design Principles and Architectural Innovations Masked jigsaw puzzle: A versatile po- sition embedding for vision transformers

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.676855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6e7a2d-7ee2-4fbe-8342-bd0139428e5a · outbound

This paper cites Bringing masked autoencoders explicit con- trastive properties for point cloud self-supervised learning.

Ultra Ethernet's Design Principles and Architectural Innovations Bringing masked autoencoders explicit con- trastive properties for point cloud self-supervised learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.837810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d06721c-fc84-43cc-a55b-6f0f55022a6f · outbound

This paper cites Info3d: Representation learning on 3d ob- jects using mutual information maximization and contrastive learning.

Ultra Ethernet's Design Principles and Architectural Innovations Info3d: Representation learning on 3d ob- jects using mutual information maximization and contrastive learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.821031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a294dbe8-5152-49fb-a9ff-b0a931544e45 · outbound

This paper cites Rl-gan-net: A reinforcement learning agent controlled gan network for real-time point cloud shape completion.

Ultra Ethernet's Design Principles and Architectural Innovations Rl-gan-net: A reinforcement learning agent controlled gan network for real-time point cloud shape completion

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.799243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9fd1be2d-1046-45df-9190-2ca8d434fd23 · outbound

This paper cites Self-supervised deep learning on point clouds by reconstructing space.

Ultra Ethernet's Design Principles and Architectural Innovations Self-supervised deep learning on point clouds by reconstructing space

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.775489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5bdb808f-5335-4d2b-a161-c49e8c36bc2f · outbound

This paper cites Earth- mind: Towards multi-granular and multi-sensor earth ob- servation with large multimodal models.

Ultra Ethernet's Design Principles and Architectural Innovations Earth- mind: Towards multi-granular and multi-sensor earth ob- servation with large multimodal models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.704758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e49fad8-74e0-4e89-b3a7-a7473a3dbb73 · outbound

This paper cites Pointgrow: Autoregressively learned point cloud generation with self- attention.

Ultra Ethernet's Design Principles and Architectural Innovations Pointgrow: Autoregressively learned point cloud generation with self- attention

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.757967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a4d0d010-8781-49e4-8850-34a32ffe7767 · outbound

This paper cites Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data.

Ultra Ethernet's Design Principles and Architectural Innovations Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.741778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e9471511-ac88-4d8e-b078-1f2185be91f8 · outbound

This paper cites Revisit- ing point cloud classification: A new benchmark dataset and classification model on real-world data.

Ultra Ethernet's Design Principles and Architectural Innovations Revisit- ing point cloud classification: A new benchmark dataset and classification model on real-world data

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.727706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3147bb38-ba9e-419c-a3a5-bcfaf86d1ee8 · outbound

This paper cites Attention is all you need.

Ultra Ethernet's Design Principles and Architectural Innovations Attention is all you need

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.726359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53d9102a-094c-4517-b522-c2067a0b4e5f · outbound

This paper cites Unsupervised point cloud pre-training via occlusion completion.

Ultra Ethernet's Design Principles and Architectural Innovations Unsupervised point cloud pre-training via occlusion completion

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.701838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b38a53d1-e0f5-4b93-9fb0-9e11b4b5eda2 · outbound

This paper cites ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models.

Ultra Ethernet's Design Principles and Architectural Innovations ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.737706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 512ac564-0d74-444a-aa26-6bcf8a169cf5 · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Dynamic graph cnn for learning on point clouds

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.686284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dc32acbd-ee97-4a6a-aa18-54c38c7763c3 · outbound

This paper cites Rolo-slam: rotation-optimized lidar-only slam in uneven ter- rain with ground vehicle.

Ultra Ethernet's Design Principles and Architectural Innovations Rolo-slam: rotation-optimized lidar-only slam in uneven ter- rain with ground vehicle

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.668016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c58ed9a0-2d4e-4767-9af3-81532e696813 · outbound

This paper cites Take-a-photo: 3d-to-2d generative pre-training of point cloud models.

Ultra Ethernet's Design Principles and Architectural Innovations Take-a-photo: 3d-to-2d generative pre-training of point cloud models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.653218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bcc1bee4-0f6a-4cc5-a76b-150cdf44a308 · outbound

This paper cites Diffusion models as masked autoencoders.

Ultra Ethernet's Design Principles and Architectural Innovations Diffusion models as masked autoencoders

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.636159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b4d6742a-03bb-4fa3-9dda-394de7f5aa56 · outbound

This paper cites Pointconv: Deep convolutional networks on 3d point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Pointconv: Deep convolutional networks on 3d point clouds

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.619267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4dff432c-8eb8-407f-b1c5-041422bdc6bf · outbound

This paper cites 3d shapenets: A deep repre- sentation for volumetric shapes.

Ultra Ethernet's Design Principles and Architectural Innovations 3d shapenets: A deep repre- sentation for volumetric shapes

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.591856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eb0c3ce0-b501-4acb-9f1a-027727ff4a52 · outbound

This paper cites Pointcontrast: Unsupervised pre- training for 3d point cloud understanding.

Ultra Ethernet's Design Principles and Architectural Innovations Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.780975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 924f18cb-79dd-4f0a-bb68-639144a82cba · outbound

This paper cites Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding.

Ultra Ethernet's Design Principles and Architectural Innovations Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.556174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 81c8608d-5d75-42db-adaa-eeeff95a0bb1 · outbound

This paper cites Gd-mae: gener- ative decoder for mae pre-training on lidar point clouds.

Ultra Ethernet's Design Principles and Architectural Innovations Gd-mae: gener- ative decoder for mae pre-training on lidar point clouds

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.537863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4bbc183d-212e-4dd7-9c8d-82bb5469cb01 · outbound

This paper cites Fold- ingnet: Point cloud auto-encoder via deep grid deformation.

Ultra Ethernet's Design Principles and Architectural Innovations Fold- ingnet: Point cloud auto-encoder via deep grid deformation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.516880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1d38768e-856c-41f1-bfbb-d76ee7a5bce8 · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling.

Ultra Ethernet's Design Principles and Architectural Innovations Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.485648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1774bbcc-8d2f-44df-8fdf-356b16cc788b · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling.

Ultra Ethernet's Design Principles and Architectural Innovations Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.465140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d33cba43-c5aa-468f-baaa-d7a4ab2e149f · outbound

This paper cites Towards compact 3d representations via point feature enhancement masked au- toencoders.

Ultra Ethernet's Design Principles and Architectural Innovations Towards compact 3d representations via point feature enhancement masked au- toencoders

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.446769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 639f27f1-0a01-4df9-88a8-77d78ba1e390 · outbound

This paper cites Online deep clustering for unsupervised representation learning.

Ultra Ethernet's Design Principles and Architectural Innovations Online deep clustering for unsupervised representation learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.427202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6ebb6807-1075-4e86-aff8-0d62b2eda51a · outbound

This paper cites Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training.

Ultra Ethernet's Design Principles and Architectural Innovations Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.838496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a569c3cc-a878-4a74-ace3-9b6d018cffb8 · outbound

This paper cites Pointclip: Point cloud understanding by clip.

Ultra Ethernet's Design Principles and Architectural Innovations Pointclip: Point cloud understanding by clip

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.403989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f0b9fe27-a70a-4a5c-9160-a02380c19808 · outbound

This paper cites PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders.

Ultra Ethernet's Design Principles and Architectural Innovations PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.848948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02c6d40f-8bf1-41b7-a05c-bbc9392cc5c5 · outbound

This paper cites Masked Surfel Prediction for Self-Supervised Point Cloud Learning.

Ultra Ethernet's Design Principles and Architectural Innovations Masked Surfel Prediction for Self-Supervised Point Cloud Learning

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.855018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fabf4422-ab76-4bc6-b1a0-aff950f5ed2a · outbound

This paper cites Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning.

Ultra Ethernet's Design Principles and Architectural Innovations Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.861874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67fe04f8-379c-4b0c-b12c-b2dfaaf1e7a1 · outbound

This paper cites Point transformer.

Ultra Ethernet's Design Principles and Architectural Innovations Point transformer

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.386226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c7121063-f6bf-493b-b4ac-c1de532b665e · outbound

This paper cites Denoising diffusion probabilistic models for action-conditioned 3d motion generation.

Ultra Ethernet's Design Principles and Architectural Innovations Denoising diffusion probabilistic models for action-conditioned 3d motion generation

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:14.875411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9142133e-bcfc-4999-b89d-0ef0e1c1709c · outbound

This paper cites Deep image clustering based on curriculum learning and density information.

Ultra Ethernet's Design Principles and Architectural Innovations Deep image clustering based on curriculum learning and density information

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.353052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bb659e6f-7848-4d1a-a7c0-349786327ef7 · outbound

This paper cites Point cloud pre-training with diffusion models.

Ultra Ethernet's Design Principles and Architectural Innovations Point cloud pre-training with diffusion models

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:34:15.328849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 4b19ef9b-1edd-4c16-aa10-8e7a73cf252c · inbound

SCENIC: Stream Computation-Enhanced SmartNIC cites this paper.

SCENIC: Stream Computation-Enhanced SmartNIC Ultra Ethernet's Design Principles and Architectural Innovations

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:48:47.171051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 30940c8c-1bd1-4865-9cba-eeccab5dcc4c · inbound

ScaleAcross: Designing Multi-Data-Center Infrastructure for Geo-Distributed AI Training cites this paper.

ScaleAcross: Designing Multi-Data-Center Infrastructure for Geo-Distributed AI Training Ultra Ethernet's Design Principles and Architectural Innovations

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T16:28:38.924116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd6711ea-2f28-422b-9090-300338298c64 · inbound

The Multipath Reliable Connection (MRC) Transport cites this paper.

The Multipath Reliable Connection (MRC) Transport Ultra Ethernet's Design Principles and Architectural Innovations

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T23:39:04.759014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.