Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
As of 3 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2412.01812.
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 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T14:05:26.032618Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T03:00:48.018147Z
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 45385a2f-9105-403e-8854-008fb58f5974 · inbound
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning cites this paper.
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T21:46:44.041463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 475a20fc-a0a8-4c6f-8f9b-68974df12f03 · inbound
All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles cites this paper.
All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
Reference 116
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T03:00:48.020920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation d7b3cdb5-44ce-4b4f-aba3-d96aa258109b · inbound
End-to-End 3-D Spatiotemporal Perception with Multimodal Fusion and V2X Collaboration cites this paper.
End-to-End 3-D Spatiotemporal Perception with Multimodal Fusion and V2X Collaboration V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T14:05:26.032618Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e812c12a-e386-493e-ae40-b47bc47c2131 · inbound
SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model cites this paper.
SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction
Reference 83
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T12:56:11.390402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.