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Integrity report for Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP

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

arXiv:2605.13436 · pith:2026:ERBMSKCVHF5ETVX2DJW5NHOQD3

0Critical
0Advisory
4Detectors run
2026-05-26Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-26 09:46:31.713660+00:00
doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-21 17:01:44.463976+00:00
doi_compliance completed v1.0.0 · findings 0 · 2026-05-20 07:13:01.928050+00:00
claim_evidence completed v1.0.0 · findings 0 · 2026-05-19 19:41:56.650598+00:00

Findings

No public integrity findings for this paper.

Signed record

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