Integrity report for Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.22591 · pith:2026:5N7ME5S4AH2P6IUJA72NTJX26P

0Critical

0Advisory

7Detectors run

2026-05-25Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

doi_compliance completed v1.0.0 · findings 0 · 2026-05-25 07:08:41.178826+00:00

doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-25 07:02:40.199038+00:00

claim_evidence completed v1.0.0 · findings 0 · 2026-05-25 01:23:38.555989+00:00

citation_quote_validity completed v0.1.0 · findings 0 · 2026-05-24 03:50:28.102918+00:00

cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-22 01:52:24.789100+00:00

shingle_duplication skipped v0.1.0 · findings 0 · 2026-05-22 01:49:51.813196+00:00

ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-05-22 01:33:37.456477+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/5N7ME5S4/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.