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Paper Citation Record · LEDGER

Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2505.04318.

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

pith.paper-citation-record.v1
2505.04318 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:35:29.314976Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T09:06:49.157464Z

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 fe2ac96d-bcbb-4e22-998d-33170440d949 · inbound

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts cites this paper.

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-07-02T09:06:49.158966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T05:40:18.458584Z digest=sha256:03b9f7f81fe176764afda93e4e859ab827a70680e03ad6d387de37e46866ecc5

Observation 3f3364cb-c42d-40be-a0c4-d1300957d10c · inbound

A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance cites this paper.

A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T11:35:29.314976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:35:29.314976Z digest=sha256:68481f5f2ec047ba471c2f8eab510a48921c0a189d7d236078562990d72895e8