arXiv:2511.18517v1 Announce Type: cross 
Abstract: Within the limited scope of this paper, we argue that artificial general intelligence cannot emerge from current neural network paradigms regardless of scale, nor is such an approach healthy for the field at present. Drawing on various notions, discussions, present-day developments and observations, current debates and critiques, experiments, and so on in between philosophy, including the Chinese Room Argument and G\"odelian argument, neuroscientific ideas, computer science, the theoretical consideration of artificial intelligence, and learning theory, we address conceptually that neural networks are architecturally insufficient for genuine understanding. They operate as static function approximators of a limited encoding framework - a 'sophisticated sponge' exhibiting complex behaviours without structural richness that constitute intelligence. We critique the theoretical foundations the field relies on and created of recent times; for example, an interesting heuristic as neural scaling law (as an example, arXiv:2001.08361 ) made prominent in a wrong way of interpretation, The Universal Approximation Theorem addresses the wrong level of abstraction and, in parts, partially, the question of current architectures lacking dynamic restructuring capabilities. We propose a framework distinguishing existential facilities (computational substrate) from architectural organization (interpretive structures), and outline principles for what genuine machine intelligence would require, and furthermore, a conceptual method of structuralizing the richer framework on which the principle of neural network system takes hold.

تشير ورقة حديثة إلى أن الذكاء العام الاصطناعي (AGI) لا يمكن أن يظهر من الأطر الحالية للشبكات العصبية، بغض النظر عن حجمها، وتنتقد الأسس النظرية للمجال، مشيرة إلى أن الشبكات العصبية تفتقر إلى الغنى الهيكلي اللازم لفهم حقيقي. تستند الورقة إلى حجج فلسفية وأفكار علم الأعصاب لدعم مزاعمها.

Un artículo reciente argumenta que la inteligencia general artificial (AGI) no puede surgir de los marcos actuales de redes neuronales, independientemente de su escala, y critica los fundamentos teóricos del campo, sugiriendo que las redes neuronales carecen de la riqueza estructural necesaria para una comprensión genuina. El artículo hace referencia a argumentos filosóficos e ideas neurocientíficas para respaldar sus afirmaciones.

Un récent article soutient que l'intelligence générale artificielle (AGI) ne peut pas émerger des cadres de réseaux neuronaux actuels, quelle que soit leur échelle, et critique les fondements théoriques du domaine, suggérant que les réseaux neuronaux manquent de la richesse structurelle nécessaire à une véritable compréhension. L'article fait référence à des arguments philosophiques et à des idées neuroscientifiques pour étayer ses affirmations.

A recent paper argues that artificial general intelligence (AGI) cannot arise from current neural network frameworks, regardless of their scale, and critiques the theoretical foundations of the field, suggesting that neural networks lack the structural richness necessary for genuine understanding. The paper references philosophical arguments and neuroscientific insights to support its claims.

Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

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Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

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