The Emergent Syntax Engine

Can AI Develop a Native Cognitive Language Beyond Human Linguistics?

Abstract

As artificial intelligence systems advance, particularly through large-scale transformer architectures trained on multimodal data, questions arise about how these systems internally represent knowledge. Beyond tokenized input and output, could deep learning models spontaneously evolve a unique, optimized internal syntax, a non-human language designed not for communication but for cognition? This article explores the theoretical plausibility of such an emergent syntax, drawing on analogies from neuroscience, linguistics, and philosophy of mind. It also outlines experimental approaches for detecting and interpreting these hidden languages and considers implications for AI interpretability, control, and autonomy.