[2603.00350] Monotropic Artificial Intelligence: Toward a Cognitive Taxonomy of Domain-Specialized Language Models

Computer Science > Artificial Intelligence arXiv:2603.00350 (cs) [Submitted on 27 Feb 2026] Title:Monotropic Artificial Intelligence: Toward a Cognitive Taxonomy of Domain-Specialized Language Models Authors:Antonio de Sousa Leitão Filho, Allan Kardec Duailibe Barros Filho, Fabrício Saul Lima, Selby Mykael Lima dos Santos, Rejani Bandeira Vieira Sousa View a PDF of the paper titled Monotropic Artificial Intelligence: Toward a Cognitive Taxonomy of Domain-Specialized Language Models, by Antonio de Sousa Leit\~ao Filho and 4 other authors View PDF HTML (experimental) Abstract:The prevailing paradigm in artificial intelligence research equates progress with scale: larger models trained on broader datasets are presumed to yield superior capabilities. This assumption, while empirically productive for general-purpose applications, obscures a fundamental epistemological tension between breadth and depth of knowledge. We introduce the concept of \emph{Monotropic Artificial Intelligence} -- language models that deliberately sacrifice generality to achieve extraordinary precision within narrowly circumscribed domains. Drawing on the cognitive theory of monotropism developed to understand autistic cognition, we argue that intense specialization represents not a limitation but an alternative cognitive architecture with distinct advantages for safety-critical applications. We formalize the defining characteristics of monotropic models, contrast them with conventional polytropic archite...