Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2501.04004.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:40:06.676272Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T23:39:03.882338Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation f5e7ae43-68d6-4710-8f2a-2192f2072366 · inbound
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media cites this paper.
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:40:06.676272Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72357686-fe9d-482c-a614-4c8e5f09b4b6 · inbound
Zero-Shot 3D Visual Grounding from Vision-Language Models cites this paper.
Zero-Shot 3D Visual Grounding from Vision-Language Models LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:12:17.070727Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bcf1c4c8-753b-473f-86b6-7d36f45fc4e4 · inbound
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers cites this paper.
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:40:26.327329Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5c4f6ec7-ed0a-4b8d-8ff3-566312cbb660 · inbound
Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation cites this paper.
Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:40:26.991409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8ee547b5-7764-4ba3-ab4a-fecdafb891e9 · inbound
OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization cites this paper.
OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T23:39:03.886167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.