Maya-Vaidya P5: Vairagya-Gated BCM Chronification Sentinel — An Antahkarana SNN Architecture for Detecting and Preventing Prosthetic-Induced Nociceptive Sensitisation

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 headline gradient, the no-free-constant claim, and the prosthetic framing.Evaluating 1 − tanh(x / 0.002315) at the paper's own EXP-A stage means gives 0.421 / 0.382 / 0.327 against the published 0.425 / 0.386 / 0.330 — the gradient is the gate formula applied to inputs reported earlier in the same paper, and could not have failed. The divisor multiplies its input by ~432, so the "graded brake" is arithmetically a step function, and a scale was chosen — borrowed from an unrelated equation with different dimensions.The dataset contains no prosthesis, no motor data and no sensitisation outcome. The gate implementation and EXP-C's honestly reported null 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2.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-Vaidya P5: Vairagya-Gated BCM Chronification Sentinel introduces a closed-loop spiking neural network built on an Advaita Vedantic cognitive architecture to detect and prevent prosthetic-induced nociceptive sensitisation — the process by which a prosthetic control loop, by amplifying pain signals beyond the nervous system's tolerance, can drive the maladaptive plasticity that turns transient discomfort into permanent chronic pain. The sole architectural addition is a single parameter-free module, the VairagyaGate (attenuation = 1 − tanh(chrono_signal / 0.002315)), which converts the passive frozen-BCM chronification tracker from Maya-Vaidya P2 into an active sentinel: as detected chronification rises, the gate releases — attenuates — afferent throughput rather than amplifying it, the Vedantic principle of vairagya (non-attachment) made computational. No new free constant is introduced. On 84 subjects (3,460 windows, PhysioPain Empatica E4, leave-one-subject-out), the gate reduced the overall chronification signal by 63.2% while preserving the Stage 3 > Stage 1 direction ordering, and — critically — braked hardest on the most chronic subjects (attenuation 0.330 vs 0.425 for acute) without ever receiving stage labels. An honest null is reported: under the canonical Bhaya threshold the activation fraction β* = 0% across all simulated load gains, with an exploratory secondary analysis (substrate-scaled threshold, fixed before load) showing β* rising monotonically from 17.3% to 93.1%. This is the fifth paper in the Maya-Vaidya Series and treats the Buddhi S-curve as a Formula Property (R² = 1.0 is a construction artifact; measured R² = 0.07–0.15 across substrates) and Bhaya Quiescence as a Reproducible Property within Antahkarana-governed architectures, per the series meta-protocol Maya-Meta P2. Honest caveats: n = 8 acute / 4 chronic subjects (whisper-level signal), single Turkish dataset, proof-of-concept. Maya-Vaidya is an India-first neuromorphic research programme targeting clinical pain analysis, and this work is a research prototype only — it is not a licensed medical device. Series: Part of the Maya-Vaidya Series — neuromorphic AI for clinical pain analysis. India-first. Research prototype only — NOT a licensed medical device. For medical emergencies in India: 112. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Vaidya-P5 and your research context) | Interactive Dashboard | FAQ | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JS ORCID: 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