Paper Citation Record · LEDGER

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data

As of 7 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.16314.

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

pith.paper-citation-record.v1
2607.16314 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:26:07.371999Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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External citation measurements

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Outbound references

Observation 5a90c89e-bdce-4f79-b3f1-5414e33d70fe · outbound

This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:04.805429Z

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Observation c704d3c5-822b-4fcb-a443-da46daddbd7d · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:04.921748Z

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Observation a8f65665-d5d1-480c-b65c-1f21ea0ec07b · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.034435Z

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Observation 112dbd3b-bb26-470a-a3c8-490844217460 · outbound

This paper cites RepVGG: Making VGG-style ConvNets Great Again.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data RepVGG: Making VGG-style ConvNets Great Again

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.111775Z

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Observation 1259a05d-2f3a-4aa3-af2d-1a2bec789946 · outbound

This paper cites seq-jepa: Autoregressive predictive learning of invariant-equivariant world models.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data seq-jepa: Autoregressive predictive learning of invariant-equivariant world models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.275033Z

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Observation 09fd7272-cb8b-4187-8e12-ca4f6fea1828 · outbound

This paper cites World Models.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data World Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.369725Z

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Observation a3473f55-b651-4e6d-80cf-9afb3a961a2d · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Dream to Control: Learning Behaviors by Latent Imagination

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.495721Z

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Observation 1f44cb60-8d53-47f5-b3ee-daf048f5b506 · outbound

This paper cites Learning Latent Dynamics for Planning from Pixels.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Learning Latent Dynamics for Planning from Pixels

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.620687Z

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Observation 842e11a1-32f3-486b-995d-fb8ce60e9c32 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Distilling the Knowledge in a Neural Network

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.750791Z

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Observation bfd85a2f-f3b8-4892-abda-a9fe302d48cb · outbound

This paper cites A Mixed Diet Makes DINO An Omnivorous Vision Encoder.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data A Mixed Diet Makes DINO An Omnivorous Vision Encoder

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.863088Z

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Observation 9ccf1645-04d4-4e00-9c8f-bb712c95ffb7 · outbound

This paper cites Enhancing End-to-End Autonomous Driving with Latent World Model.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Enhancing End-to-End Autonomous Driving with Latent World Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:05.968550Z

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Observation 0b83fd7e-78c0-4522-82a0-eb030e4941e0 · outbound

This paper cites LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:06.151401Z

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Observation 843d1e97-eb7b-44d2-a6f4-14d5a2b99948 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data DINOv2: Learning Robust Visual Features without Supervision

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:06.280601Z

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Observation 42960ff1-e226-4347-88de-e1545849c598 · outbound

This paper cites Tartanground: A large-scale dataset for ground robot perception and navigation.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Tartanground: A large-scale dataset for ground robot perception and navigation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:06.430491Z

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Observation 8cd4fe41-00a2-4f4f-a707-11ac168a0b2a · outbound

This paper cites Learning from reward-free offline data: A case for planning with latent dynamics models.arXiv preprint arXiv:2502.14819, 2025.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Learning from reward-free offline data: A case for planning with latent dynamics models.arXiv preprint arXiv:2502.14819, 2025

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:06.630169Z

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Observation 99a6e6b6-09ee-4aa3-bb0a-308daa6455b5 · outbound

This paper cites Understanding self-supervised Learning Dynamics without Contrastive Pairs.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Understanding self-supervised Learning Dynamics without Contrastive Pairs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:06.743589Z

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Observation 5b63f2e0-60fc-4aa9-9334-b9acf01812e5 · outbound

This paper cites MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:06.835803Z

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Observation 44855899-f39d-4a36-818b-54708130812c · outbound

This paper cites A new learning paradigm: Learning using privileged information.Neural Networks, 22(5–6):544–557, 2009.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data A new learning paradigm: Learning using privileged information.Neural Networks, 22(5–6):544–557, 2009

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:06.988946Z

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Observation e16df18f-8b4d-49b5-ac20-40de3b5d5419 · outbound

This paper cites MobileOne: An Improved One millisecond Mobile Backbone.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data MobileOne: An Improved One millisecond Mobile Backbone

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:07.067135Z

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Observation 494a20fd-e69d-45db-9f83-7cc66dacf5fa · outbound

This paper cites World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:07.155612Z

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Observation 2d818f8c-4859-49b3-999f-7b0c8e6c9d9d · outbound

This paper cites DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:07.242925Z

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Observation e9803679-2181-4994-b24b-a8c2c8e3db6a · outbound

This paper cites Ad-l-jepa: Self- supervised spatial world models with joint embedding predictive architecture for autonomous driving with lidar data.

Depth-Regularized JEPA World Models Learn More Transferable Representations from Real Outdoor Robot Data Ad-l-jepa: Self- supervised spatial world models with joint embedding predictive architecture for autonomous driving with lidar data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T05:26:07.371999Z

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Pith citing papers

No inbound Pith citation observations are available.