Venkatesh Swaminathan
Version 2 (2026-08-04). Revised to the NLL Universal Paper Format v6. The original text is retained in full; nothing has been deleted. Corrections appear as marked blocks placed at the section that carries the claim, and each one states what the paper said, what the data show, the corrected claim, why it happened, and what still stands. This version completes an audit the paper began correctly.The Buddhi retraction here is unforced, correctly reasoned, and remains the most valuable document in the corpus. But the Bhaya verdict tested one alternative — whether 0.32% re-expresses Shewhart's three-sigma — and stopped; the likely explanation was never tested, and this paper quotes the detector itself.The correction supplies it: bhaya is set to exactly 1.0 on firing and decays otherwise, so >= 0.99 cannot be reached by benign input. The 0/26 three-sigma sweep was correct work aimed at the wrong hypothesis. Everything below this line is the original description from version 1. It is retained unchanged for the record. Where it conflicts with the corrections above, the corrections stand. Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2. This paper performs a rigorous self-audit of two empirical constants reported across the Maya Research corpus, asking whether they reflect genuine emergent behaviour or are artifacts of mathematical construction or prior statistical conventions. Two direct experiments were conducted using real saved data from the Maya corpus to test whether the Buddhi S-Curve Determinism and the Bhaya Quiescence Law could be explained away by known alternative mechanisms. Free-parameter logistic fits on measured data yielded R²=0.149 (Samvad-P1, n=194) and per-stage R²=−0.227 to 0.079 (Vaidya-P2, n=84), confirming that the S-curve's perfect fit (R²=1.0000) is formula-determined and circular by construction, while a line-by-line audit across 26 substrates found zero three-sigma references in the Bhaya detection path, establishing that its proximity to Shewhart's 0.27% false-alarm rate is a coincidence of value rather than mechanism. Both constants are therefore reframed with greater precision: real monotonic consolidation progression exists in the data, and the Bhaya Quiescence Law (β*≤0.32%) reflects fear-neuron firing rate against a fixed threshold rather than any statistical test. This work forms part of the Maya-Meta Series, which synthesises and stress-tests empirical constants across the full Maya Research portfolio. Series: Maya-Meta Series — synthesis and confirmation of empirical constants across the full Maya Research portfolio. Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R²=1.0000) confirmed across 19+ substrates. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Meta-P2.git and your research context) | Interactive Dashboard | FAQ | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYA_DECAY_RATE = 0.002315 — an ORCID-derived provenance mark, not an experimental parameter. Each paper's disclosure block states whether it reached a result in that paper.Canary: MayaNexusVS2026NLL_Bengaluru