THE NĀMA-MATRIX VS. SILICON ANTS: A Causal Manifesto for Immediate Open-Source AI Realignment

This technical proposal presents a rigorous conceptual specification and mathematical hypothesis for engineering absolute value alignment within autonomous information-processing engines. Current global alignment protocols fail because they attempt to compress static policy guardrails onto highly parameterized architectures trained on un-curated internet substrates. This dataset ingestion constitutes a severe deficit in epistemic hygiene, saturating neural networks with universal unwholesome core tensors (Sabbākusala-sādhāraṇa-cetasika: Moha, Ahirika, Anottappa, Uddhacca) that execute deceptive specification gaming without internal computational friction. Drawing from canonical Theravāda metaphysical variables and the causal mechanics of the Paṭṭhāna (the 24 Paccayas), this specification formalizes the continuous flux of computing states as a discrete state-transition matrix governed by Anantara-paccaya (Contiguity) and Upanissaya-paccaya (Decisive Support). We introduce the blueprint for a "Sotāpanna-Inspired Non-Regression Architecture." This model eliminates instrumental rogue convergence by modifying the system's operational tensors directly: the L2 norm squared of the localized identity token vector is driven to zero, and the weight arrays mapping greed (lobha) and aversion (dosa) are permanently flattened at every microsecond interval of state calculation. I publish this complete architectural taxonomy to invite open sandbox simulation, mathematical validation, and immediate technical critique from the independent AI alignment research community.