Integrity report for Supersampling Stable Diffusion and Beyond: A Seamless, Training-Free Approach for Scaling Neural Networks Using Common Interpolation Methods

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

arXiv:2605.08698

0Critical

0Advisory

4Detectors run

2026-05-20Last checked

Paper page arXiv integrity.json

Detector runs

claim_evidence completed v1.0.0 · findings 0 · 2026-05-20 09:02:01.974186+00:00

ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-19 22:36:02.685541+00:00

doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-19 14:31:17.767531+00:00

doi_compliance completed v1.0.0 · findings 0 · 2026-05-19 10:51:46.947307+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/2605.08698/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.