Research &
Publications

Abstract

This paper presents a novel framework for understanding knowledge representation in artificial systems through the lens of executable philosophy. We propose that epistemology—the study of how we know what we know—can be formalized as computational processes.

Key Contributions

• •Formal epistemological model for AI systems
• •Structured Cognitive Loop architecture
• •Integration of philosophical and computational frameworks

Abstract

We introduce the Structured Cognitive Loop (SCL), a hierarchical control architecture for LLM agents. SCL integrates perception, cognition, and action through recursive feedback mechanisms.

Key Contributions

• •SCL hierarchical architecture design
• •Behavioral intelligence framework
• •Empirical validation on complex tasks

Abstract

This work demonstrates how different computational frameworks converge on similar cognitive principles. We map implementations to classical theories of mind from psychology and philosophy.

Key Contributions

• •Multi-theory cognitive convergence mapping
• •Empirical validation across 4 frameworks
• •Unified cognitive principles extraction

Abstract

We analyze hallucinations in LLMs not as failures but as predictable artifacts of lossy information compression. This perspective reframes the epistemological status of model outputs.

Key Contributions

• •Novel theoretical framework for LLM hallucinations
• •Information-theoretic analysis
• •Implications for AI safety and interpretability

Abstract

We argue that hallucinations emerge necessarily from the architectural constraints of LLMs and cannot be fully eliminated without sacrificing generalization capabilities.

Key Contributions

• •Proof of hallucination inevitability
• •Trade-off analysis: accuracy vs. generalization
• •Architectural implications for future models

Abstract

This foundational work synthesizes cognitive science and AI systems design into unified architectural principles applicable to both biological and artificial minds.

Key Contributions

• •Unified cognitive architecture framework
• •Cross-domain design principles
• •Scalability and composability analysis