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Paper Citation Record · LEDGER

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation

As of 11 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2505.16080.

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

pith.paper-citation-record.v1
2505.16080 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:11:22.091659Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

11 of 11 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7839ffe4-3c3f-42fe-b5d6-dfc036dd6f9c · outbound

This paper cites Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:22.001903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:22.001903Z digest=sha256:98e2b65992425c633cc86c943baa6dd90c1e850084918fe38da98486fc66d8df

Observation 6f16a6f6-80f6-4434-8e20-5f8b4cb25a23 · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:22.068117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:22.068117Z digest=sha256:0cfd5382051cfd1f0ae66775e0197e94509a2b86faa1569f19c3c26e7360c7aa

Observation 6a38dca4-8a1c-4051-82de-b1ded59968b9 · outbound

This paper cites Spatial-Temporal Transformer Networks for Traffic Flow Forecasting.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Spatial-Temporal Transformer Networks for Traffic Flow Forecasting

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:22.075545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:22.075545Z digest=sha256:6f6be1a76bbd2d9c05d6d9079ce95e6a9495f25406f78ef20c169c3953d7b5e1

Observation 29ff96ac-2c4b-4927-9cda-f660b8553a05 · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:22.083797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:22.083797Z digest=sha256:dd88dac461f50ce95e7573e1eb7baa7e064bd1391c49d56243696e3d3d79a245

Observation db6f436c-ff36-49e0-aa3e-6a7961179383 · outbound

This paper cites an unresolved cited work.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:11:22.343599Z

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.

source=pdf_text observed=2026-08-07T15:11:22.091659Z digest=sha256:f9f5eefef9bcd245157a576d7c010412db0582751ee08ccaee5df37599c9071a

Observation 2aad8805-d234-4982-bb16-2249f5394c9b · outbound

This paper cites The information bottleneck method.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation The information bottleneck method

Reference 2000

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:22.061641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:22.061641Z digest=sha256:7b5a7793b51e3a30716e00ac95cf93610b2eaf9594f2eccdd7dd29423b486e93

Observation 67d655f8-eb03-4710-9f99-4117c5162167 · outbound

This paper cites an unresolved cited work.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Unresolved cited work

Reference 2009

Resolution
unresolved
raw_fallback, observed 2026-08-07T15:11:22.427238Z

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.

source=pdf_text observed=2026-08-07T15:11:22.010922Z digest=sha256:2a30b7bfdaaaf8193547b4440507e6ed18b78996db827e4889abb63d037b889b

Observation 9c5d86b5-7cdd-4364-9124-ff8113df9c03 · outbound

This paper cites Dimensionality reduction by learning an invariant mapping.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Dimensionality reduction by learning an invariant mapping

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:11:22.391985Z

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.

source=pdf_text observed=2026-08-07T15:11:22.039702Z digest=sha256:58fa07a0eb182ff6f1d332b2187071c386dabc980f3619262e690bde0496e052

Observation f8954e97-e195-4483-acb4-167742bc0eb3 · outbound

This paper cites Supervised Contrastive Learning.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Supervised Contrastive Learning

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:22.054018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:22.054018Z digest=sha256:4f61bc647680d47ad6df06d1ec94dd31ff36d5ef460ec32a3a411f3e2c022e4e

Observation 1c571a1b-a11f-43bf-971e-76dd7f2e7de0 · outbound

This paper cites Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

Reference 2021

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T15:11:22.256177Z

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.

source=pdf_text observed=2026-08-07T15:11:22.033414Z digest=sha256:bf22d10c6b2fd0d13475af042efeeac79479c9fc8a7fcaa7650fd35dcb968a20

Observation 0daec4d6-5f7d-4e05-a2a2-78a33142cfbf · outbound

This paper cites TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning.

SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:22.018635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:22.018635Z digest=sha256:3a18d9bd985740b6c9c2b8caf8d9d116d7b64ef0650db1a4ec819b481fe589d4

Pith citing papers

No inbound Pith citation observations are available.