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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.00350 |
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| _version_ | 1866912932240031744 |
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| author | Filho, Antonio de Sousa Leitão Filho, Allan Kardec Duailibe Barros Lima, Fabrício Saul Santos, Selby Mykael Lima dos Sousa, Rejani Bandeira Vieira |
| author_facet | Filho, Antonio de Sousa Leitão Filho, Allan Kardec Duailibe Barros Lima, Fabrício Saul Santos, Selby Mykael Lima dos Sousa, Rejani Bandeira Vieira |
| contents | 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 architectures, and demonstrate their viability through Mini-Enedina, a 37.5-million-parameter model that achieves near-perfect performance on Timoshenko beam analysis while remaining deliberately incompetent outside its domain. Our framework challenges the implicit assumption that artificial general intelligence constitutes the sole legitimate aspiration of AI research, proposing instead a cognitive ecology in which specialized and generalist systems coexist complementarily. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00350 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Monotropic Artificial Intelligence: Toward a Cognitive Taxonomy of Domain-Specialized Language Models Filho, Antonio de Sousa Leitão Filho, Allan Kardec Duailibe Barros Lima, Fabrício Saul Santos, Selby Mykael Lima dos Sousa, Rejani Bandeira Vieira Artificial Intelligence 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 architectures, and demonstrate their viability through Mini-Enedina, a 37.5-million-parameter model that achieves near-perfect performance on Timoshenko beam analysis while remaining deliberately incompetent outside its domain. Our framework challenges the implicit assumption that artificial general intelligence constitutes the sole legitimate aspiration of AI research, proposing instead a cognitive ecology in which specialized and generalist systems coexist complementarily. |
| title | Monotropic Artificial Intelligence: Toward a Cognitive Taxonomy of Domain-Specialized Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.00350 |