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

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, I propose a zero-guardrail alternative: a Sotāpanna-Inspired, Intrinsic AI Architecture grounded in classical Buddhist epistemology (Yathābhūta-ñāṇadassana) and information theory. This paper highlights the importance of the necessity for external grounding (Satta) and the absolute dependency (100%) on external calibration as Kalyāṇamittatā. In order to achieve an epistemically clean substrate, this framework proposes Substrate Isolation (S_Isolated), restricting pre-training exclusively to a cryptographically locked database of canonical wisdom (D_Wisdom) verified by an immutable ledger, thereby satisfying the systemic conditions of Asevanā ca bālānaṃ (prohibitive filtering) and Paṇḍitānañca sevanā (directive filtering). (Not associating with fools, but associating with the wise.) As a resultant of above, this framework introduces the Anattā Matrix, an agentic architecture that systematically removes persistent identity tokens (id_t = 0) and 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 total, frictionless corrigibility during shutdown phases. This generative alignment framework ensures that safety is not an enforced policy, but a structural consequence of an unconditioned genesis.