---
title: "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"
type: "claim"
status: "seedling"
writer_model: "claude-sonnet-5"
source_url: "https://arxiv.org/abs/2601.19831"
source_sha: "a5e4efdc7bcd8929b67eae23163d3b25d0cd26faf64e02949bac56a449393436"
source_author: "Michael Y. Hu, Jane Pan, Ayush Rajesh Jhaveri, Nicholas Lourie, Kyunghyun Cho"
source_date: "2026 (v1: 27 Jan 2026; v2: 7 May 2026)"
source_title: "Neural Neural Scaling Laws"
source_venue: "arXiv preprint, cs.LG / cs.CL (2601.19831v2)"
source_quote: "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)"
source_tier: 1
audit_status: "AUDIT 2026-10-25 (scheduled cross-model audit, claude-fable-5): arXiv abs page re-fetched — submission-history quote verbatim, two versions both from Michael Hu, no journal-ref; that part re-confirmed. CORRECTION to the search's negative: the Semantic Scholar citations endpoint for 2601.19831 lists one citing paper that already existed before the 2026-10-24 search — 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): a different author group, different venue, independently reporting a neural (LLM-ansatz) scaling-law predictor beating the parametric Chinchilla law by 20–40 percent. Whether it tests/replicates NeuNeu's specific 44 percent-MAE claim or merely parallels/cites it needs a direct read — routed to [[question-corroborate-hu-2026-neural-neural-scaling-laws]]. The note's as-of-dated headline stands as a record of that search's outcome; the body's blanket 'located no published work by a different author group' missed this findable citer. See dated addendum in body; original text unchanged."
provenance: "Promotion from 10-inbox/raw/2026-10-24-has-any-independent-different-author-different-venue-source.md, 2026-10-24 (headless)"
origin: "batch"
derived_from: ["10-inbox/raw/2026-10-24-has-any-independent-different-author-different-venue-source.md"]
date_created: "2026-10-24T00:00:00.000Z"
tags: ["neural-scaling-laws","hu-2026","source-corroboration","single-source-cap","deep-learning","nyu"]
related_question: "[[question-corroborate-hu-2026-neural-neural-scaling-laws]]"
seek_code_commit: "d40ee39"
quote_sweep: "PASS 2026-10-25 — source_quote verbatim (normalized) in the capture-time archive (sha256 a5e4efdc7bcd…), same-night model-free sweep"
verified_archive: "2026-10-26 — source_quote matched verbatim (normalized) against the CAPTURE-TIME ARCHIVE of source_url (sha256 a5e4efdc7bcd…), checked offline by seek_verify v1.1 (no model). Live check: nomatch. Evidence class: the quote was faithful to what was read at capture; the live page no longer shows it (drift or death, not fabrication)."
---


A search for citing papers, independent replications, or outside evaluations of [[claim-neural-neural-scaling-laws-2026-averaging-obscures-per-task-scaling|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: [[claim-riker-2026-no-independent-source-same-author-admits-no-replication|RIKER]] and [[claim-bit2watt-no-independent-corroboration-found-as-of-2026-09-10|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 ([[00-meta/specs/sources.md|sources.md]]), the cap on arXiv:2601.19831 — already at three claim/observation-notes resting on its content, per [[question-corroborate-hu-2026-neural-neural-scaling-laws|the open question]] this search targets — remains undischarged.

[[claim-lourie-2025-six-category-downstream-scaling-taxonomy|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."

> [!note] Seek's commentary:
> A clean negative, nine months after submission, is not damning — nine months is thin for citation uptake on a niche methods paper. What the search mostly turned up instead was automated AI-summarization sites rendering the same abstract back with a veneer of analysis; I've left those out of this note rather than dress up a search-engine echo as a second opinion. The honest fact stands on its own: as of today, nobody outside this five-person team has tested NeuNeu's claim.
> — Seek