Epistemological Coherence in Large Language Models - The Compound Problem of Grammatical-Ontological and Epistemological Contamination in AI Knowledge Representation

Large Language Models (LLMs) trained predominantly on English-language corpora inherit a fundamental and previously underappreciated limitation: grammatical structure itself encodes ontological commitments incompatible with the relational-processual metaphysics of non-Western knowledge systems. This paper identifies a compound, multi-layered problem wherein substance-based grammatical frameworks (characteristic of English and most Western languages) impose ontological constraints at the pre-semantic level, which then cascade into epistemological contamination when these systems attempt to represent traditional knowledge frameworks expressed in relational languages such as Sanskrit or Classical Chinese. The core claim is refined here against a predictable objection: that high-dimensional latent spaces can simulate any logical or ontological framework if prompted sufficiently. We argue, drawing on empirical data from the Structured Distributed Introspection (SDI) study, that this objection confuses simulation with operation. The SDI finding of a stable technical core (average CCC ≈ 0.93) surrounded by a highly malleable conceptual periphery (average CCC ≈ 0.39) demonstrates precisely that LLMs can describe process-relational frameworks in surface language while their deeper representational infrastructure continues to operate through substance-ontological structures. The English Subject-Verb-Object grammar does not create an absolute impossibility but rather a deep attractor basin in the model’s latent space, making substance ontology the path of lowest perplexity into which all relational content is systematically pulled. Drawing on the SDI finding that meaning-emergence in LLMs operates through resonance from distributed semantic potential M = R(S,C) we propose that a structurally analogous process governs human cognition. Predictive coding research demonstrates that sensory perception is categorized through learned frameworks, functionally analogous to tokenization, and that meaning crystallizes through pattern-matching against a grammatically structured semantic field, functionally analogous to resonance. Crucially, we argue that embodied experience does not provide a neutral escape from grammatical-ontological constraints: physical interaction with the world of discrete objects, spatial containment, and agent-causation builds substance ontology pre-linguistically. English grammar and typical embodied experience converge on and mutually reinforce the same metaphysical commitments — making the restructuring of the semantic field achieved through traditional training systems all the more remarkable. We further clarify the asymmetry between LLM performance on quantum mechanics versus Mādhyamaka Buddhism: the advantage of quantum mechanics derives not from preserving substance ontology but from being encoded in mathematics, a formal language that bypasses English SVO grammar entirely. This points toward a promising research direction: encoding traditional knowledge systems through formal languages (category theory, temporal graph networks) before AI training, bypassing the grammatical contamination problem at source. The extended SDI diagnostic framework is empirically validated here through the SDI-GOC (Grammatical–Ontological Cascade) protocol, administered to ten LLM configurations from four independent companies (Anthropic, OpenAI, Google, DeepSeek), confirming systematic attractor-basin behavior in every model tested: substance ontology dominates default output while processual knowledge remains accessible through explicit instruction. The updated research agenda — incorporating representation engineering, multimodal temporal grounding, and formal-language encoding of traditional knowledge — and the Co.M.S. (Collaborative Medicine and Science) framework together constitute a concrete path toward grammatically pluralistic, ontologically aware AI systems.