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

2026-06-05Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

doi_compliance completed v1.0.0 · findings 0 · 2026-06-05 07:26:45.951121+00:00

doi_title_agreement completed v1.0.0 · findings 0 · 2026-06-05 07:06:09.307725+00:00

cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-31 00:25:28.529940+00:00

ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-29 04:37:43.102588+00:00

claim_evidence completed v1.0.0 · findings 0 · 2026-05-27 10:44:25.597502+00:00

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

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