Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-12T18:45:36.595398Z
Paper Citation Record · LEDGER
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
As of 26 July 2026, this Paper Citation Record lists 4 of 4 outbound references and 1 inbound Pith citation observation for arXiv:2604.22851.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-26T06:30:07.085553+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-08T20:09:58.785097Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T20:15:34.268730Z
Reference resolution
4 of 4 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 118d9ecb-2db0-4cf3-9c44-d43e2b7612c1 · outbound
This paper cites U-Net: Convolutional Net- works for Biomedical Image Segmentation,.
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving U-Net: Convolutional Net- works for Biomedical Image Segmentation,
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T18:45:36.595398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 213eb0c2-a66f-4499-8f65-0671afc66a75 · outbound
This paper cites 3D U-Net: Learning Dense V olumetric Segmentation from Sparse Annotation,.
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving 3D U-Net: Learning Dense V olumetric Segmentation from Sparse Annotation,
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T18:45:36.595398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e4d9953-245d-463a-8906-b8d3bcfb3073 · outbound
This paper cites V-Net: Fully Convolutional Neural Networks for V olumetric Medical Image Segmentation,.
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving V-Net: Fully Convolutional Neural Networks for V olumetric Medical Image Segmentation,
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T18:45:36.595398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e611c75e-49fa-467a-a905-7c6d0a95b899 · outbound
This paper cites Attention U-Net: Learning Where to Look for the Pancreas.
EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving Attention U-Net: Learning Where to Look for the Pancreas
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T18:45:36.595398Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 8a27b63f-7ff5-4de2-9ee8-a504dcecf99c · inbound
Imagined Rollouts are Kinematic, Not Dynamic: A Diagnosis of Long-Horizon World-Model Failure cites this paper.
Imagined Rollouts are Kinematic, Not Dynamic: A Diagnosis of Long-Horizon World-Model Failure EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T20:15:34.270457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-26T06:30:07.085553+00:00.