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

Antonio de Sousa Leitão Filho, Allan Kardec Duailibe Barros Filho, Fabrício Saul Lima, Selby Mykael Lima dos Santos, Rejani Bandeira Vieira Sousa

TL;DR

This 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.

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 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.

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

TL;DR

This 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.

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 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.
Paper Structure (35 sections, 2 theorems, 2 equations, 3 tables)

This paper contains 35 sections, 2 theorems, 2 equations, 3 tables.

Table of Contents

  1. Introduction
  2. Theoretical Foundations
  3. The Epistemology of Generalist AI
  4. Monotropism as Cognitive Architecture
  5. From Human to Artificial Monotropism
  6. Formal Definition of Monotropic AI
  7. Epistemological Critique of the Scaling Paradigm
  8. The Conflation of Capability and Reliability
  9. The Impossibility of Universal Grounding
  10. The Safety Implications of Unbounded Competence
  11. Design Principles for Monotropic Systems
  12. The Principle of Intentional Exclusion
  13. The Principle of Physical Grounding
  14. The Principle of Structured Verification
  15. The Principle of Minimal Size
  16. ...and 20 more sections

Key Result

Proposition 2

For a fixed model capacity $C$, domain coverage $D$, and domain-specific performance $P_d$, a monotropic architecture achieves $P_d^{mono} > P_d^{poly}$ for $d \in D^*$ (the focused domain) while accepting $P_d^{mono} < P_d^{poly}$ for $d \notin D^*$. Under the assumption that domains are sufficient

Theorems & Definitions (5)

  • Definition 1: Monotropic Attentional Allocation
  • Proposition 2: Monotropic Specialization Trade-off
  • Definition 3: Monotropic Language Model
  • Conjecture 4: Capability-Reliability Trade-off
  • Proposition 5: Safety Advantage of Bounded Competence