Maya-Meta P1: The Bhaya Quiescence Law and Buddhi S-Curve Determinism — Two Empirical Constants Confirmed Across 19 Substrates in Affective Neuromorphic Computing
Year: 2026
Type: software
Abstract: 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 [email protected] 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 (embedded in all canonical hyperparameters)Canary: MayaNexusVS2026NLL_Bengaluru_Narasimha
Author: Swaminathan, Venkatesh
Institution: Birla Institute of Technology and Science, Pilani
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7163065830