Maya-Meta P1: The Bhaya Quiescence Law and Buddhi S-Curve Determinism — Two Empirical Constants Confirmed Across 19 Substrates in Affective Neuromorphic Computing

This version marks the paper superseded: both claimed Laws are withdrawn.I retracted the Buddhi S-Curve in Maya-Meta P02 on 2026-06-10 — "circular by construction", R² = 0.149 against real data and −0.227 in the acute stratum — and that retraction never reached this record until now; the Bhaya Quiescence Law is withdrawn because detection fires at bhaya >= 0.99 on an effectively binary variable, so thirteen exact zeros are a threshold setting rather than a convergence.Retained in full as a record of what was claimed. It should not be cited as a source. One counted substrate (Chinmaya) has no DOI and remains unpublished. 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. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2. Maya-Meta P1 presents the first formal synthesis of empirical constants discovered across the Maya Research Series, asking whether the same quantitative patterns emerge when 19 fundamentally different computational substrates — spanning spiking neural networks, large language models, echo state networks, operating system schedulers, sleep monitors, and wound-healing networks — are examined together. The answer is yes: two constants appeared independently in every fixed-topology substrate tested, without being designed in. The Bhaya Quiescence Law establishes that aggregate crisis rates converge to β* ≤ 0.32% under stable conditions across all 16 substrates and 28 papers tested, while Buddhi S-Curve Determinism establishes that discriminative consolidation follows D(t) = 1/(1+exp(−8.0×(t−0.45))), rising from 0.030 to 0.988 with R²=1.0000, confirmed across spiking, language model, and reservoir substrates alike. This paper is the inaugural entry in the Maya-Meta Series, which synthesises and confirms empirical constants across the full Maya Research portfolio spanning 19 published studies from 2026. The convergence of both constants across substrates as diverse as military LLM inference and EEG-based sleep staging suggests these may reflect substrate-independent organisational principles in affective neuromorphic computing rather than artefacts of any single architecture. 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-P1 and your research context) | 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