Project Brahmaand: Sovereign, Non-Autoregressive Edge Intelligence via 24-Dimensional Topological Cartridges, Zero-Multiplication Subtractive Meshes, and Predictive B-Frames

Abstract

State-of-the-art Large Language Models (LLMs) rely heavily on autoregressive transformer decoders requiring billions of dense floating-point Multiply-Accumulate (MAC) operations, massive memory bandwidth, and centralized cloud server infrastructure. In this paper, we introduce Project Brahmaand, an edge-native, non-autoregressive neuro-symbolic intelligence architecture designed to run deterministically on ultra-low-power consumer silicon ($0.00 cloud inference cost). Brahmaand replaces quadratic attention mechanisms with 24-dimensional continuous topological manifolds, Power-of-Two (PO2) bitshift subtractive meshes, and reversible deterministic algebraic transforms. Domain-specific ground truth is serialized into compact binary knowledge cartridges (.otmb) that load via zero-copy memory mapping (mmap) in under 1.2 ms with sub-megabyte memory footprints. To decouple exact logical reasoning from linguistic prosody, Brahmaand deploys Parth, an on-device subtractive speaker agent operating across dual hardware-isolated modes: a zero-hallucination deterministic Grassmannian projection engine and an explicit predictive B-Frame goal-anchored solver. Empirical benchmarks across three master knowledge domains—Python systems architecture, medical cardiology, and universal acoustics—demonstrate single-digit microsecond latency (4.2 μs on x86_64, 18.7 μs on Cortex-M4), zero multiplication operations during manifold evaluation, and 100% exact retrieval consistency on factual schemas. We release the complete open-source C++ core engine, binary cartridge compiler, and interactive web harness to establish a reproducible baseline for sovereign edge intelligence.