The Precision-Layer Architecture (PLA): A Coherence-Driven Computational Framework Beyond Control-Based Transformer Logic
Year: 2026
Type: article
Abstract: The Precision-Layer Architecture (PLA) proposes a stabilization framework for agentic AI systems that prioritizes precision over coarse control. Rather than enforcing behavioral constraints through top-down steering, PLA introduces operator-level mechanisms designed to preserve cross-scale consistency, keep mismatch observable, and bound drift dynamics. This document defines architectural commitments, formal operators, and observable stability criteria without claiming omniscience or biological equivalence. PLA reframes safety as detectability of mismatch rather than enforcement of predefined outcomes. PLA is substrate-neutral in the sense that it requires structural primitives rather than a specific implementation class or hardware architecture. Detailed parametrization and physical realization (including oscillatory integration windows, coherence fields, non-linear wave-based implementation, and scale-growth operators) are deferred to a separate technical note. The work is intended as a contribution to ongoing discussions on agentic AI governance, robustness, and architectural safety design. Keywords: agentic AI stability architecture drift detection mismatch observability cross-scale consistency precision-first stabilization AI safety complex systems cybernetics predictive processing AI Assistance and Research Provenance: Generative AI assisted in the mathematical formalization and notation of author-developed conceptual relations in the Precision Layer Architecture. The underlying structural relations and their intended functions were defined by the author; AI systems supported their translation into mathematical and formal representations.
Author: Becker, Diana
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7128911888