Maya-OS: An Affective Spiking Neural Network as a Conversational Operating System Arbitration Layer

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 withdraws the paper's headline emergence claim."Trust suppresses fear" is the arbitration cascade executing as written: §4.4 lists PROTECT above KILL and the code confirms both the threshold and the early return, so the finding is the if-statement read back out; the r = 0.154 correlation is also withdrawn as non-discriminating.The leaky-integrator behaviour, the tick-level logging, and the honest report that KILL and SUSPEND never fired all stand. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2.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-OS is the first affective Spiking Neural Network deployed as a live conversational operating system arbitration layer, using four Leaky Integrate-and-Fire neurons — each encoding a distinct emotional-cognitive state drawn from Advaita Vedantic philosophy — to govern real-time process scheduling, resource protection, and system call decisions without any hardcoded decision logic. Every operating system action emerges from the continuous dynamics of an affective voltage vector propagated through a shared heterosynaptic weight matrix, replacing traditional rule-based schedulers with biologically inspired neural arbitration. Across five experimental sessions totalling 133 ticks of continuous operation, the system demonstrated autonomous threat accumulation, Shraddha-mediated escalation suppression preventing aggressive process termination in 100% of co-active threat events, confirmed LIF action potential discharge with voltage reset, intent-driven neural modulation from conversational input, and autonomous homeostatic trust recovery following forced suppression. This paper is part of the Maya Research Series — 13 papers implementing the Advaita Vedantic Antahkarana as computational primitives in spiking neural networks — with the Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R² = 1.0000) confirmed across the series. Series: Part of the Maya Research Series — 13 papers implementing the Advaita Vedantic Antahkarana as computational primitives in spiking neural networks. Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R²=1.0000) confirmed across all papers. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-OS 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