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Integrity report for Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

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

arXiv:2606.04238 · pith:2026:R355RZMMPT4MJ7C4MJLWNCUQQW

0Critical
0Advisory
4Detectors run
2026-06-05Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

claim_evidence completed v1.0.0 · findings 0 · 2026-06-05 04:49:15.565048+00:00
citation_quote_validity skipped v0.1.0 · findings 0 · 2026-06-04 23:51:10.574655+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-06-04 16:27:28.837967+00:00
ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-06-04 02:35:37.660573+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/R355RZMM/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.