SNT-LIFE: A Digital Learning Organism

SNT-LIFE: A Learning Digital Organism presents a unified operator framework for learning, memory, and behavior, demonstrating that seven fundamental operators—fluctuation (), cyclic reset (), phase nexter (), phase reverser (), liminal thresholding (), irreversible loss (), and subspace mapping () —form a numerically closed operator algebra at machine precision (4. 82 10^-15). Original Data: All results are validated against empirical datasets: · C. elegans connectome (Cook et al. 2019, 300 neurons, 3, 707 synapses) · Whole-brain calcium imaging (Kato et al. 2015, 5 recordings, 60 s each) · LongBench multi-document QA benchmark for LLM evaluation Key Achievements: · Biological validation: Variance spectrum correlation r = 0. 986 with Kato data, PC1 = 42. 3% (vs Kato 43. 8%), using only 7 parameters—an 86× reduction vs Wilson-Cowan (600 parameters) · Generalization: r > 0. 95 on all five Kato recordings without retuning · Circuit validation: AVB-motor r = +0. 857, AVA-motor r = -0. 700 from connectome structure, not fitted · Artificial validation: SNT-MEM achieves 63. 3\% 2. 1\% memory reduction and 3. 8 0. 3 retrieval speedup on Llama-3-8B · Soft-Clamp homeostatic mechanism: Stabilizes threshold at = 0. 477 0. 095, eliminating blackout instability (0. 874 without Soft-Clamp) · Necessity theorem: Learning requires consolidation () or pruning () ; ablation shows removal causes 78. 3\% loss, Soft-Clamp removal causes 34. 2\% loss · Lie algebraic closure: 7-element basis \I, B, C, W, W, B, W, C, B, C\ closes at 4. 82 10^-15; 4-element basis fails (residual > 0. 99) Impact: The framework provides a unified mathematical language for describing learning across scales—from a worm learning to avoid a toxin to a large language model consolidating knowledge—with potential applications in neuroscience, artificial intelligence, and quantum error correction.