TCW-OS: A Living-AI Operating System for Microcontrollers via Genome-Phenotype Weight Decomposition

We present TCW-OS, an operating system for managing the lifecycle of neural network weights on microcontrollers. The architecture is based on genome-phenotype decomposition W = (C, Φ, b), where C is a frozen invariant core (genome), Φ is a trainable adapter (phenotype, 25 weights = 100 bytes), and b is an expression gate. The system implements 8 formal operations over Φ: metabolism (E_topo), regeneration (R), aging (A), mutation (M), proliferation (P), normalization (N), composition, and Elastic Weight Consolidation (EWC). Every operation preserves C and modifies only Φ. TCW-OS is built on Zephyr RTOS 4.5 with TensorFlow Lite Micro and runs on real hardware: Seeed XIAO ESP32-C3 (RISC-V @ 160 MHz, 400 KB SRAM, ~$2). All 8 operations execute in 10 preemptive threads. Six measured results, reproduced on both x86 (native_sim) and RISC-V (ESP32-C3): 1. Homeostasis of C is empirically proven: max|Δy_model| = 0.00000000 after all 8 operations, on both architectures. 2. Genome-phenotype separation is proven: children share y_model = 1.025116 but differ in y_final (0.969 / 0.668 / 0.777). 3. Initialization is critical for Fisher: zero-init yields Fisher = 1/25 (dead gradient); random init [-0.1, 0.1] yields 25/25. 4. Multi-anchor EWC is harmful at MCU scale: at λ = 1000, multi-anchor forgetting = +62.4%, single-anchor = +26.4% (36 pp difference). 5. The λ curve is non-monotonic: +33.7 / +48.5 / +63.7 / +26.4% for λ = 0 / 50 / 200 / 1000. A "dead zone" exists at λ ∈ [50, 500]. 6. Production-ready footprint: 284 KB Flash (3.4%), 104 KB RAM (26%), full lifecycle in 2–3 minutes.