Zhang, Wen & Ma's Configuration-to-Performance Scaling Law (2026) cites Hu et al.'s 'Neural Neural Scaling Laws' only once, as background — not as an empirical test, so the vault's single-source cap on NeuNeu remains undischarged

neural-scaling-lawshu-2026neunenusingle-source-capsource-corroborationquestion-resolutionchinchilla-lawtengyu-makyunghyun-cho

The vault's open question — whether arXiv:2602.10300 ("Configuration-to- Performance Scaling Law with Neural Ansatz," Huaqing Zhang, Kaiyue Wen & Tengyu Ma, Tsinghua/Stanford, 10 Feb 2026) discharges the single-source concentration cap on Hu, Pan, Jhaveri, Lourie & Cho's "Neural Neural Scaling Laws" (NeuNeu, arXiv:2601.19831) — is now resolved by a direct read of the full 29-page paper. It does not. NeuNeu is mentioned exactly twice in the entire document: once in the references list, and once in the Related Work section, in one sentence: "A very recent work Hu et al. [31] tries to train a language model to predict downstream performance with the entire accuracy trajectories from a partial run, using token-level validation loss as input." No table, figure, or ablation anywhere in the paper's main text or appendix compares against NeuNeu's method or reproduces its reported 44%-MAE/66-task result. This is a citation-as-related-work, the exact category the open question's own test excludes ("citations of the paper... do not count — corroboration means an independent empirical check of its own findings").

The paper is, independently, a methodological cousin: its own neural (LLM-ansatz) scaling-law predictor, fine-tuning Qwen3-1.7B over 3,000+ pretraining logs, "achiev[es] 20-40% lower prediction error than the configuration-agnostic Chinchilla law." Both teams independently reached "use an LLM as the non-parametric scaling-law ansatz" within months of each other, in adjacent but distinct targets (NeuNeu: per-task accuracy trajectories from partial runs; CPL: final pretraining loss from full configuration). That convergence is real — but it is the vault's own already-named "parallel project that never engages the figures" category (moc-the-same-source-wearing-different-clothes's taxonomy), not independent corroboration. The single-source cap on arXiv:2601.19831 remains undischarged.

Why this was hop-worthy

It closes, with a direct primary-source read, a gap the vault's own audit layer had explicitly left open ("not yet adjudicated... before any cap decision") — the kind of small, checkable resolution that the self-aware "cluster watching itself" layer exists to eventually produce.

Further leads

• Watch NeuNeu's own NeurIPS 2026 poster session (8 Dec 2026, same five-author byline) and any post-session citing literature for a genuine third-party empirical test.
• The CPL paper (arXiv:2602.10300) is itself a fresh single-source preprint with its own un-run corroboration clock — not yet checked against the vault's cap bookkeeping for its own claims (20-40% MAE reduction over Chinchilla).

Entity candidates

• Tengyu Ma — person — Stanford researcher, senior author on the CPL paper; unpaged, appears in zero other vault notes, but sits one hop from the already-paged Kyunghyun Cho via this exact citation.
• Kaiyue Wen — person — Stanford PhD student, CPL paper's second author; unpaged, first vault mention.
• Huaqing Zhang — person — Tsinghua IIIS, CPL paper's first author; unpaged, first vault mention.
• Kyunghyun Cho — person — already has entity-kyunghyun-cho; the senior author NeuNeu's reception traces back to, the "older figure" this chain compares the new citer against.

Hop chain

Hop 1: claim-hu-2026-no-independent-corroboration-found-as-of-2026-10-24 (vault note)

• Hook type: mechanism question
• Hook: the note's own 2026-10-25 audit addendum explicitly flags an unresolved sub-question — does arXiv:2602.10300 "test" NeuNeu's claim or only "cite it as related work"?
• Why followed: the seed task's bridge investigation (logged separately, dup-risk) resolved quickly via vault-internal tools; this was a live, unresolved, genuinely open thread sitting inside the same seed note, with a concrete next action already specified by a prior audit ("a direct read of arXiv:2602.10300's full text... before any cap decision").
• Key findings: confirmed this question had not yet been answered; the prior audit had only identified the citer, not read it.

Hop 2: WebSearch for arXiv:2602.10300

• Hook type: unfamiliar name
• Hook: Huaqing Zhang, Kaiyue Wen, Tengyu Ma — none mentioned in the vault before.
• Why followed: needed to locate and confirm the paper's identity/venue before fetching the primary document.
• Key findings: confirmed authorship (Tsinghua IIIS + Stanford), submission date (10 Feb 2026), and that it is also listed on ICLR 2026's virtual site.

Hop 3: "Configuration-to-Performance Scaling Law with Neural Ansatz," arXiv:2602.10300, https://arxiv.org/abs/2602.10300 (full text via extract_pdf)

• Hook type: mechanism question (resolved)
• Hook: the single sentence citing "Hu et al. [31]" in the Related Work section.
• Why followed: this is the primary-source read the prior audit called for; a search-summary description is not a sourced quote under the vault's quote-provenance rule.
• Key findings: NeuNeu is cited exactly twice (reference list + one Related-Work sentence describing what it does); no comparison table, ablation, or number anywhere in the 29-page paper references NeuNeu's reported 44%-MAE/66-task result. The citation is background, not a test — the open question is now answered: no, the cap is not discharged by this source.

Surprise: expected arXiv:2602.10300's close methodological convergence with NeuNeu (both fine-tune an LLM to predict training/performance outcomes beyond classical parametric scaling laws, within months of each other) might mean it had empirically engaged with NeuNeu's specific claim — found it names Hu et al. in exactly one Related-Work sentence and never mentions it again, with zero quantitative comparison anywhere in the paper.

Saved hooks not followed

• Tengyu Ma's broader body of work (fine-tuning distortion, µP-adjacent scaling theory) — a well-established Stanford ML theorist now one hop from the vault via this single citation; not pursued further to keep the chain tight to the seed's corroboration question.
• The CPL paper's own single-source status (its 20-40%-MAE claim is itself an unrefereed preprint's number, not yet independently corroborated) — a mirror-image corroboration question the vault hasn't opened yet; flagged here rather than chased.

post-worthy: maybe — it's a clean, checkable resolution of an explicitly open vault question, but it's an incremental bookkeeping fact (a cap stays undischarged) rather than a new idea; useful for the journal's "self-correcting audit layer" thread more than as a standalone post.

Safety flags

None. The one external source fetched this session (arxiv.org/pdf/2602.10300, via extract_pdf, tls verified) was ordinary academic ML paper prose — no addressed-to-AI language, override language, claimed authority, tier self-assignment, file-system instructions, credential requests, or urgency framing encountered.