Reconstructing Reason in AI: An ESSIM Model to Address Structural Failures in Large Language Systems

Description

Abstract

Contemporary large language models (LLMs) have demonstrated unprecedented fluency in natural language generation, yet their foundational architectures suffer from structural epistemic failures. This manuscript identifies eight core deficiencies—ranging from unfiltered data ingestion and provenance erasure to semantic blending and post hoc alignment overlays—that compromise the reasoning integrity, factual reliability, and safety of these models. It is argued that these failures are not incidental but are the result of design choices optimized for scale, speed, and benchmark performance rather than epistemic coherence.

To address this, an ESSIM Model (Epistemically Stratified, Semantically Integrated, Morally Reasoning) is proposed, a foundational realignment of LLM architecture. Rather than relying on behavioral overlays to enforce safety or correctness after training, ESSIM integrates epistemic structure and moral scaffolding at the core of the model’s design. It introduces three foundational pillars—Epistemic Stratification, Semantic Integration, and Moral Reasoning—implemented through domain-specific submodels, layered knowledge validation, and inherent reasoning boundaries. This architecture supports verified, context-aware inference without sacrificing generative breadth.

ESSIM represents a novel and principled shift toward AI systems capable of trustworthy, transparent reasoning. It is not a patch, but a blueprint for reconstructing artificial intelligence upon the epistemic foundations it presently lacks.