What remains when everything inessential has been released? Śūnyatā does not destroy. It completes. We present Maya-Śūnyatā, the eighth paper in the Maya Research Series, which extends our affective spiking neural network (SNN) architecture for class-incremental learning (CIL) on Split-CIFAR-100 with two new mechanisms. Karma (कर्म) is introduced as the first second-order plasticity history signal in the series: the absolute integral of per-synapse weight trajectory changes accumulated across tasks. A synapse that has been repeatedly pulled in conflicting directions by successive tasks carries that interference in its weight trajectory. Karma makes this interference legible to the architecture. Śūnyatā (शून्यता) is the corresponding structured pruning mechanism: at each task boundary, synapses whose Karma exceeds a Buddhi-modulated threshold are zero-masked. The Buddhi modulation produces an emergent developmental arc — young Maya prunes aggressively, mature Maya conservatively: effectiveₜhreshold = baseₜhreshold × (0. 5 + buddhi × 0. 5). Biological grounding: microglial phagocytosis via the C1q/C3 complement cascade. A seven-condition ablation on Split-CIFAR-100 (10 tasks, seed=42) establishes four findings. Condition C (continuous pruning) equals Condition A (baseline) exactly — AA=15. 19%, zero pruning — confirming that Karma must accumulate across a full task to reach threshold; continuous batch-level pruning never fires. Condition F (Karma replacing Chitta) yields AA=9. 06%, proving that structural pruning and gradient gating are complementary, not substitutable. Condition E (aggressive threshold=0. 03) prunes 98. 48% of fc1 by Task 9, confirming the spike starvation failure mode. Condition D★ (Vairagya-gated Karma) proves the philosophically necessary interaction: synapses with accumulated Vairagya protection are spared from Karma pruning, yielding AA=10. 39% and pruned=59. 28% versus 62. 01% for canonical Condition D — Vairagya moderates Karma, exactly as Vedanta prescribes, though Maya at 21 has not yet accumulated the Vairagya of a 50-year-old. We confirm, for the eighth consecutive paper, that Bhaya quiescence under replay is a series-level constant — the Bhaya Quiescence Law. Buddhi’s S-curve consolidation gate is confirmed as architecturally deterministic across all seven ablation conditions. Code, ablation scripts, interactive bilingual dashboard, and steganographically signed figures are available at https: //github. com/venky2099/Maya-Shunyata.
Venkatesh Swaminathan (2026) studied this question.