Paper Citation Record · LEDGER

SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2211.12509.

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

pith.paper-citation-record.v1
2211.12509 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:15:58.967248Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:22:59.074285Z

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 e76e0e47-9fd2-42dc-a4e9-a2ab40a2832c · inbound

Data-driven Precipitation Nowcasting Using Satellite Imagery cites this paper.

Data-driven Precipitation Nowcasting Using Satellite Imagery SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T14:57:03.651038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ac1c495-fa3f-43d8-8993-b1a0d62e8239 · inbound

Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting cites this paper.

Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T11:00:56.613231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab372e4-2bf0-43c7-b64e-8ecf2c194cad · inbound

High-throughput digital twin framework for predicting neurite deterioration using MetaFormer attention cites this paper.

High-throughput digital twin framework for predicting neurite deterioration using MetaFormer attention SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T13:12:23.548363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42dad57b-67d1-4975-bb7d-7da2bf0f5278 · inbound

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting cites this paper.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:15:58.967248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb2ff63-f01c-4725-87a4-c5fde8fa7bbd · inbound

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation cites this paper.

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:22.573125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b10e8d-1093-424e-a74d-0a27d23a36a2 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 144

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:21:25.935342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 169d93c3-6d46-4f18-8c74-b68dd7abd3f8 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 144

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:22:59.076607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.