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

We present Maya-Meta P1, the first formal synthesis of empirical constants emerging from the Maya Research Series and its extensions. Across 19 published papers and 28 Zenodo records spanning 2026, two constants appeared without design in every fixed-topology substrate tested: the Bhaya (भय) Quiescence Law and Buddhi (बुद्धि) S-curve determinism. The Bhaya Quiescence Law states that the aggregate crisis rate β converges to β* ≤ 0.32% under stable operating conditions, independent of substrate class. Confirmed across 16 substrates spanning spiking neural networks, large language models, echo state networks, OS arbitrators, sleep EEG systems, and clinical pain biosignal processors. Zero exceptions in fixed-topology substrates. Buddhi S-curve determinism states that the discriminative consolidation trajectory follows D(t) = 1/(1+exp(−8.0×(t−0.45))), rising from 0.030 to 0.988, R²=1.0000. Confirmed across SNN (P4–P9), LLM (Phi-2 LoRA), ESN (Chinmaya, forthcoming), and Sleep EEG (Maya-Nidra P1). Three morphogenetic substrates deviate from the Bhaya law (4.87%–15.46%) — mechanistically explained by wound-response bioelectric physics. Maya-Vaidya P1 records β = 52.3%, characterised as an active open research question. VAIRAGYA_DECAY_RATE = 0.002315 (derived from ORCID 0000-0002-3315-7907) is embedded in all canonical hyperparameters as provenance anchor. Both laws are falsifiable, measurable with maya-metrics, and immediately applicable. The canonical 19-substrate dataset and maya-metrics v2 are released with this paper. GitHub: github.com/venky2099/Maya-Meta-P1Series: Maya-Meta Series, Paper 1Institution: Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 Series: Maya-Meta Series, Paper 1. Synthesis of two empirical constants confirmed across 19 substrates: the Bhaya Quiescence Law (β≤0.32%) and Buddhi S-Curve Determinism (R²=1.0000). Covers 28 Zenodo records. Links: GitHub Repository | 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

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-31
DOI
https://doi.org/10.5281/zenodo.20477224
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Venkatesh Swaminathan
Zenodo (CERN European Organization for Nuclear Research)
Advanced Memory and Neural Computing
article

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

Venkatesh Swaminathan
article en

Abstract

We present Maya-Meta P1, the first formal synthesis of empirical constants emerging from the Maya Research Series and its extensions. Across 19 published papers and 28 Zenodo records spanning 2026, two constants appeared without design in every fixed-topology substrate tested: the Bhaya (भय) Quiescence Law and Buddhi (बुद्धि) S-curve determinism. The Bhaya Quiescence Law states that the aggregate crisis rate β converges to β* ≤ 0.32% under stable operating conditions, independent of substrate class. Confirmed across 16 substrates spanning spiking neural networks, large language models, echo state networks, OS arbitrators, sleep EEG systems, and clinical pain biosignal processors. Zero exceptions in fixed-topology substrates. Buddhi S-curve determinism states that the discriminative consolidation trajectory follows D(t) = 1/(1+exp(−8.0×(t−0.45))), rising from 0.030 to 0.988, R²=1.0000. Confirmed across SNN (P4–P9), LLM (Phi-2 LoRA), ESN (Chinmaya, forthcoming), and Sleep EEG (Maya-Nidra P1). Three morphogenetic substrates deviate from the Bhaya law (4.87%–15.46%) — mechanistically explained by wound-response bioelectric physics. Maya-Vaidya P1 records β = 52.3%, characterised as an active open research question. VAIRAGYA_DECAY_RATE = 0.002315 (derived from ORCID 0000-0002-3315-7907) is embedded in all canonical hyperparameters as provenance anchor. Both laws are falsifiable, measurable with maya-metrics, and immediately applicable. The canonical 19-substrate dataset and maya-metrics v2 are released with this paper. GitHub: github.com/venky2099/Maya-Meta-P1Series: Maya-Meta Series, Paper 1Institution: Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 Series: Maya-Meta Series, Paper 1. Synthesis of two empirical constants confirmed across 19 substrates: the Bhaya Quiescence Law (β≤0.32%) and Buddhi S-Curve Determinism (R²=1.0000). Covers 28 Zenodo records. Links: GitHub Repository | 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

Zenodo (CERN European Organization for Nuclear Research)
Birla Institute of Technology and Science, Pilani (IN)
Reduced inequalities, Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Advanced Memory and Neural Computing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.