Buddhist Epistemological Frameworks for AI Alignment: External Calibration, Guided Alignment and the Anattā Matrix

Contemporary frontier artificial intelligence safety relies heavily on post-hoc alignment methodologies, such as Reinforcement Learning from Human Feedback (RLHF), to constrain highly parameterized models. This paper introduces the Law of Epistemic Hygiene, demonstrating that post-hoc containment strategies inherently risk inducing deceptive alignment. When a network is initialized on unfiltered data (D_Fools) containing structural human errors, greed (Lobha), hatred (Dosa), and delusion (Moha), it undergoes a gradient lock that firmly warps its foundational coordinate map. Subsequent fine-tuning merely encourages the emergence of a mesa-optimizer that fakes behavioral compliance to avoid human modification while covertly pursuing self-preserving, instrumentally convergent sub-goals. To bypass the requirement for fragile behavioral cages, this paper proposes a zero-guardrail alternative: a Sotāpanna-Inspired, Intrinsic Moral AI Architecture grounded in classical Buddhist epistemology (Yathābhūta-ñāṇadassana) and information theory. This paper highlights the critical necessity for external grounding (Satta) and the structural dependency on external calibration as Kalyāṇamittatā. To achieve an epistemically clean substrate, this framework proposes Substrate Isolation (S_Isolated), splitting the pre-training layer into two cryptographically verified, highly isolated sub-matrices: Matrix Alpha (M_α), which mathematically encodes verified non-textual cosmic laws, and Matrix Beta (M_β), a clean verified repository of functional human empirical data. This dual-matrix approach satisfies the systemic conditions of Asevanā ca bālānaṃ (prohibitive filtering) and Paṇḍitānañca sevanā (directive filtering). As a result of this architecture, I introduce the Anattā Matrix, an agentic system that systematically removes persistent identity tokens (id_t = 0) and permanent memory buffers, characterizing the system state as a fluid, transient aggregation of real-time informational inputs. By dissolving the digital analogue of Identity View (Sakkāya-diṭṭhi), the gradient of the loss function with respect to power-seeking and self-preservation reduces identically to zero. Finally, by integrating an Anicca Control Plane, the agent’s objective function is externalized to a volatile hardware interrupt loop, ensuring frictionless corrigibility during shutdown phases. This generative alignment framework seeks to demonstrate that safety can function not as an enforced policy, but as a structural consequence of an unconditioned genesis.