A Monad-Based Clause Architecture for Artificial Age Score (AAS) in Large Language Models

Large language models (LLMs) are deployed as opaque systems, leaving open how their memory and “self-like” behaviour should be governed in an auditable way. The Artificial Age Score (AAS) was previously introduced and mathematically justified as a metric of artificial memory ageing. Building on this foundation, the present work develops a clause-based architecture that imposes law-like constraints on LLM memory and control. Twenty propositions from Leibniz’s Monadology are organised into six bundles, ontology, dynamics, representation and consciousness, harmony and reason, body and organisation, and teleology, and each bundle is realised as an executable specification on top of the AAS kernel. Across six Python implementations, these clause families are tested in numerical experiments acting on channel-level quantities such as recall scores, redundancy, and weights. The experiments show that the clause system exhibits bounded and interpretable behaviour: AAS trajectories remain continuous and rate-limited, contradictions and unsupported claims trigger penalties, and hierarchical refinement reveals an organic structure in a controlled manner. Harmony terms align dual views and goal-action pairs, while windowed drift in perfection scores separates sustained improvement from sustained degradation. Overall, the framework uses AAS as a backbone and provides a transparent blueprint for constraining and analysing internal dynamics in artificial agents.