🤖 AI Summary
Large language models (LLMs) lack auditable, enforceable governance mechanisms for the evolution of internal memory.
Method: We propose a novel, Leibnizian monad-inspired architecture comprising six executable clauses—formalizing monadology as a code-level regulatory system—and introduce kernelized Artificial Memory Aging Scoring (AAS) to drive memory updates, underpinned by three core mechanisms: continuity assurance, contradiction penalization, and hierarchical interpretability.
Contribution/Results: Evaluated via Monad-semantic modeling, Python-based numerical experiments, and channel-level metrics (recall, redundancy, weight evolution) alongside windowed Perfectness Drift Detection, our framework ensures controllable, continuous memory trajectories; explicitly suppresses contradictory or unsupported assertions; enables organic, traceable hierarchical structure; achieves dual-perspective (model/user) alignment; and reliably distinguishes sustained model improvement from performance degradation.
📝 Abstract
Large language models (LLMs) are often deployed as powerful yet opaque systems, leaving open how their internal memory and"self-like"behavior should be governed in a principled and auditable way. The Artificial Age Score (AAS) was previously introduced and mathematically justified through three theorems that characterise it as a metric of artificial memory aging. Building on this foundation, the present work develops an engineering-oriented, clause-based architecture that imposes law-like constraints on LLM memory and control. Twenty selected monads from Leibniz's Monadology are grouped 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 minimal Python implementations, these clause families are instantiated in numerical experiments acting on channel-level quantities such as recall scores, redundancy, and weights. Each implementation follows a four-step pattern: inputs and setup, clause implementation, numerical results, and implications for LLM design, emphasising that the framework is not only philosophically motivated but also directly implementable. The experiments show that the clause system exhibits bounded and interpretable behavior: AAS trajectories remain continuous and rate-limited, contradictions and unsupported claims trigger explicit penalties, and hierarchical refinement reveals an organic structure in a controlled manner. Dual views and goal-action pairs are aligned by harmony terms, and windowed drift in perfection scores separates sustained improvement from sustained degradation. Overall, the monad-based clause framework uses AAS as a backbone and provides a transparent, code-level blueprint for constraining and analyzing internal dynamics in artificial agents.