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Integrity report for A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models

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

arXiv:2605.14929 · pith:2026:B6LWCBTYIAAM6NENFJZAHJTVHI

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

Paper page arXiv integrity.json bundle.json

Detector runs

ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-05-26 06:39:59.336346+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-24 19:54:39.546929+00:00
claim_evidence completed v1.0.0 · findings 0 · 2026-05-20 15:02:05.484513+00:00
citation_quote_validity skipped v0.1.0 · findings 0 · 2026-05-19 11:49:43.850638+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/B6LWCBTY/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.