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

• URL: http://arxiv.org/abs/2512.11835v1
• Date: Wed, 03 Dec 2025 12:48:40 GMT
• Title: A Monad-Based Clause Architecture for Artificial Age Score (AAS) in Large Language Models
• Authors: Seyma Yaman Kayadibi,
• Abstract summary: This work develops an engineering-oriented, clause-based architecture that imposes law-like constraints on large language models.<n>Twenty selected monads from Leibniz's Monadology are grouped into six bundles.<n>Six minimal Python implementations are instantiated in numerical experiments acting on channel-level quantities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models [96.0074341403456]
  Inference-time compute has re-emerged as a practical way to improve LLM reasoning.<n>Most test-time scaling (TTS) algorithms rely on autoregressive decoding.<n>We propose Prism, an efficient TTS framework for dLLMs.
  arXiv  Detail & Related papers  (2026-02-02T09:14:51Z)
• FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning [20.27406245916013]
  We propose a novel framework that unifies the structural inductive biases of decision forests with the semantic reasoning capabilities of large language models (LLMs)<n>Our method is two-fold. First, we introduce a semantic splitting criterion in which the LLM evaluates candidate partitions based on their coherence over both labeled and unlabeled data, enabling the induction of more robust and generalizable tree structures under few-shot supervision.<n>Second, we propose a one-time in-context inference mechanism for leaf node stabilization, where the LLM distills the decision path and its supporting examples into a concise, deterministic prediction, replacing noisy empirical estimates with semantically informed outputs
  arXiv  Detail & Related papers  (2026-01-16T14:08:51Z)
• Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling [51.56484100374058]
  We argue that Executable Ontologies (EO) represent a transition from algorithmic behavior programming to semantic world modeling.<n>We show how EO achieves prioritybased task interruption through dataflow conditions rather than explicit preemption logic.
  arXiv  Detail & Related papers  (2026-01-12T19:57:35Z)
• Table-BiEval: A Self-Supervised, Dual-Track Framework for Decoupling Structure and Content in LLM Evaluation [11.450834626205676]
  Table-BiEval is a novel approach based on a human-free, self-supervised evaluation framework.<n>It calculates Content Semantic Accuracy and Normalized Tree Edit Distance to decouple structure from content.<n>Results reveal substantial variability, highlighting that mid-sized models can surprisingly outperform larger counterparts in structural efficiency.
  arXiv  Detail & Related papers  (2026-01-09T07:38:27Z)
• BRIDGE: Building Representations In Domain Guided Program Verification [67.36686119518441]
  BRIDGE decomposes verification into three interconnected domains: Code, Specifications, and Proofs.<n>We show that this approach substantially improves both accuracy and efficiency beyond standard error feedback methods.
  arXiv  Detail & Related papers  (2025-11-26T06:39:19Z)
• Bridging Symbolic Control and Neural Reasoning in LLM Agents: The Structured Cognitive Loop [0.0]
  We introduce Structured Cognitive Loop (SCL), a modular architecture that separates agent cognition into five phases: Retrieval, Cognition, Control, Action, and Memory (R-CCAM)<n>At the core of SCL is Soft Symbolic Control, an adaptive governance mechanism that applies symbolic constraints to probabilistic inference.<n>We provide a complete open-source implementation demonstrating the R-CCAM loop architecture, alongside a live GPT-4o-powered travel planning agent.
  arXiv  Detail & Related papers  (2025-11-21T05:19:34Z)
• Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms [51.56484100374058]
  We compare two approaches to modeling robotic behavior: imperative Behavior Trees (BTs) and declarative Executable Ontologies (EO)<n>BTs structure behavior hierarchically using control-flow, whereas EO represents the domain as a temporal, event-based semantic graph driven by dataflow rules.
  arXiv  Detail & Related papers  (2025-11-19T09:38:01Z)
• Leibniz's Monadology as Foundation for the Artificial Age Score: A Formal Architecture for Al Memory Evaluation [0.0]
  This paper develops a mathematically rigorous, philosophically grounded framework for evaluating artificial memory systems.<n>Building on a previously formalized metric, the Artificial Age Score (AAS), the study maps twenty core propositions from the Monadology to an information-theoretic architecture.
  arXiv  Detail & Related papers  (2025-11-09T10:48:33Z)
• CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension [55.29309306566238]
  Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents.<n>This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents.<n>We draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured schemata, flexible assimilation, and dynamic accommodation.
  arXiv  Detail & Related papers  (2025-10-07T02:16:30Z)
• Implicit Reasoning in Large Language Models: A Comprehensive Survey [67.53966514728383]
  Large Language Models (LLMs) have demonstrated strong generalization across a wide range of tasks.<n>Recent studies have shifted attention from explicit chain-of-thought prompting toward implicit reasoning.<n>This survey introduces a taxonomy centered on execution paradigms, shifting the focus from representational forms to computational strategies.
  arXiv  Detail & Related papers  (2025-09-02T14:16:02Z)
• Do LLMs Dream of Discrete Algorithms? [0.7646713951724011]
  Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence.<n>Their reliance on probabilistic inference limits their effectiveness in domains requiring strict logical reasoning.<n>This paper proposes a neurosymbolic approach that augments LLMs with logic-based reasoning modules.
  arXiv  Detail & Related papers  (2025-06-29T22:03:01Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• SymbolicAI: A framework for logic-based approaches combining generative models and solvers [9.841285581456722]
  We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. We treat large language models (LLMs) as semantic solvers that execute tasks based on both natural and formal language instructions.
  arXiv  Detail & Related papers  (2024-02-01T18:50:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.