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Integrity report for Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning

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

arXiv:2606.26989 · pith:2026:B5FERLK6GA3TMPMOLUDOEQ2OH3

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Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/B5FERLK6GA3TMPMOLUDOEQ2OH3/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.