Cognitive AI framework 2.0: advances in the simulation of human thought

TL;DR

The paper introduces Cognitive AI Framework 2.0, a unified architecture that enhances personalization, coherence, and reasoning in AI by integrating memory, governance, and reasoning modules without altering large language models.

Contribution

It presents a novel unified memory architecture with governance mechanisms and differentiated reasoning modules, advancing scalable, ethical, and adaptable cognitive AI systems.

Findings

Enhanced contextual continuity across sessions.

Improved reasoning capabilities through specialized modules.

Robust knowledge management with governance mechanisms.

Abstract

The Human Cognitive Simulation Framework proposes a governed cognitive AI architecture designed to improve personalization, adaptability, and long-term coherence in human AI interaction. The framework integrates short-term memory (conversation context), long-term memory (interaction context), cognitive processing modules, and managed knowledge persistence into a unified architectural model that ensures contextual continuity across sessions and controlled accumulation of relevant information. A central contribution is a unified memory architecture supervised by explicit governance mechanisms, including algorithmic relevance validation, selective persistence, and auditability. The framework incorporates differentiated processing modules for logical, creative, and analogical reasoning, enabling both structured task execution and complex contextual inference. Through dynamic and selective knowledge updating, the system augments the capabilities of large language models without modifying their internal parameters, relying instead on retrieval augmented generation and governed external memory. The proposed architecture addresses key challenges related to scalability, bias mitigation, and ethical compliance by embedding operational safeguards directly into the cognitive loop. These mechanisms establish a foundation for future work on continuous learning, sustainability, and multimodal cognitive interaction. This manuscript is a substantially revised and extended version of the previously released preprint (DOI:10.48550/arXiv.2502.04259).