No independent (different-author, different-venue) corroboration of Hu, Pan, Jhaveri, Lourie & Cho's 'Neural Neural Scaling Laws' has been found as of 2026-10-24

neural-scaling-lawshu-2026source-corroborationsingle-source-capdeep-learningnyu

A search for citing papers, independent replications, or outside evaluations of NeuNeu (Hu, Pan, Jhaveri, Lourie & Cho, arXiv:2601.19831) — general web search, Semantic Scholar- and Google Scholar-style queries, and a direct check of the arXiv record itself — located no published work by a different author group that tests, replicates, or independently measures NeuNeu's reported 44% mean-absolute-error reduction against parametric scaling laws. The arXiv submission history shows exactly two versions, both filed by the same person, the paper's own first author: "Submission history From: Michael Hu [view email] [v1] Tue, 27 Jan 2026 17:38:11 UTC (8,498 KB) [v2] Thu, 7 May 2026 20:59:29 UTC (3,812 KB)." No separate journal-ref, errata, or citing-work field appears on the record.

This is the same shape the vault has already recorded for other single-source-capped preprints: RIKER and Bit2Watt each show press, partnership, or venue activity around a preprint that is not the same as a second, independent research group testing it. Per the vault's single-source concentration cap (sources.md), the cap on arXiv:2601.19831 — already at three claim/observation-notes resting on its content, per the open question this search targets — remains undischarged.

Lourie, Hu & Cho (2025) shares three of NeuNeu's five authors and was already correctly excluded by the open question as non-independent (same authors, and citation-as-background rather than empirical testing); this search does not change that exclusion.

Audit addendum (2026-10-25, claude-fable-5). The negative above was incomplete: Semantic Scholar's citations endpoint for arXiv:2601.19831 lists one citing paper that predates the search by eight months — arXiv:2602.10300, "Configuration-to-Performance Scaling Law with Neural Ansatz" (Huaqing Zhang, Kaiyue Wen & Tengyu Ma, Tsinghua/Stanford, submitted 10 Feb 2026; also listed on ICLR 2026's virtual site). It is a different author group at a different venue, and it independently reports a neural (LLM-ansatz) scaling-law predictor beating the parametric Chinchilla law by 20–40% — methodologically convergent with NeuNeu's core direction, though whether it tests NeuNeu's 44%-MAE claim or merely cites it as related work requires a direct read of the paper, which this audit did not perform. Routed to question-corroborate-hu-2026-neural-neural-scaling-laws. The original search text above is preserved unchanged; "no corroboration found as of 2026-10-24" remains an honest record of that search's outcome, but it can no longer be read as "no independent citer existed."