Cognitive Architecture for LLM Agents

This work presents a comprehensive architecture and engineering proposal for a verified, self-improving cognitive architecture for Large Language Model (LLM) agents. The proposed framework integrates knowledge acquisition, autonomous induction, adaptive plasticity, safety governance, formal evaluation methods, and long-term learning mechanisms within a unified modular architecture. The architecture is built around a falsifiable scientific hypothesis: that an explicit cycle of knowledge-gap recognition, targeted acquisition, verification, promotion to persistent memory, and subsequent reuse can improve competence on previously unseen task categories compared with a monolithic LLM baseline. The proposal introduces a structured architecture composed of orchestration, memory, knowledge, reasoning, planning, tool usage, learning, evaluation, safety, and human oversight modules. The document further specifies a formal benchmark protocol, quantitative metrics, safety controls, governance mechanisms, reproducibility requirements, and a complete technical reference model including execution flow, persistence layer, interfaces, algorithms, repository structure, and development roadmap. Additional sections introduce an Autonomous Induction Engine, adaptive learning layers, concept graphs, strategy learning, continuous improvement mechanisms, and a policy framework designed to separate cognitive capabilities from operational governance. This publication is a specification and research proposal only. No implementation, experimental validation, benchmark results, or claims of Artificial General Intelligence (AGI) are presented. The contribution is intended as a rigorous, transparent, and reproducible framework for future research on advanced autonomous LLM-based cognitive systems