How BAMA Elevates Local AI to Frontier Performance — Cortical Amplification: Zero-Decompression 256-Byte Cortical Graphs in Q(sqrt(5)), the 90/10 Notebook Context Window Split, and 4-Tier Cognitive Compilation on 4B–9B Local Models

How BAMA Elevates Local AI to Frontier Performance — Cortical Amplification: Zero-Decompression 256-Byte Cortical Graphs in Q(√5), the 90/10 Notebook Context Window Split, and 4-Tier Cognitive Compilation on 4B–9B Local Models U.S. Patent Pending: USPTO Provisional Patent Application No. 64/180,009 (Filed October 20, 2026). Large Language Models running locally on consumer hardware (4B–9B parameters) suffer from severe reasoning degradation when raw episodic history, dense RAG chunks, and tool schemas compete inside the active attention window. We introduce BAMA (Bare-Metal Agentic Memory Architecture), a hardware-aligned cognitive memory and compilation framework that transforms compact local models into frontier-grade reasoning engines: Zero-Decompression 256-Byte Cortical Cells in Z[φ] ⊂ Q(√5) (ZDC-QEGG): 768-D embeddings are projected into a 64-D golden-ratio quadratic integer lattice (a + b*φ), achieving a 12.0× memory footprint reduction, 0.9999935 cosine fidelity, and sub-millisecond (0.044 ms) L1/L2 cache-line cosine retrieval with zero runtime decompression. 90/10 Notebook Context Window Split & Dual-Cap B.I.F.R.O.S.T. 4-Tier Cognitive Compilation (W1–W4) & 160-Kernel Master Collective Graph Library (40,960 Bytes). 6-Tier Empirical Evaluation: +23.2 pp quality lift on Gemma 4 E4B (74.6% to 97.8%), +22.4 pp lift on Qwen 3.5 9B (76.8% to 99.2%), and 100.0% Peak Trinity Synergy across 15 benchmark dimensions.