• tag:aim-pro.eu,2026:oer/924141c704d1
• https://arxiv.org/abs/2511.18517
• arxiv:2511.18517

This engine's id, the source URI, and any persistent identifier the source published.

• Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

The title of this resource, not of the record or repository it came from.

• dcmitype:Text
• preprint

A DCMI Type term for the form held, plus the source's own genre label as declared.

• Khanh Gia Bui

Only agents the source named as creators. A repository owner is not made an author by default.

• 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 Gödelian 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.

The abstract, and the depositor's additional notes after it when the source has a field for them.

• cs.AI (arxiv)
• cs.LG (arxiv)

Declared keywords and taxonomy terms, each carrying its scheme.

• en

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• http://creativecommons.org/licenses/by/4.0/

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• CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive

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• open

The access condition the source declared. Not the licence.

• text/markdown

One value per form held — the original and the extracted text.

• 231298 bytes (extracted text)

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• 2025-11-23

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• arXiv:2403.15529
• arXiv:2102.09798
• arXiv:2507.07156
• arXiv:2006.05205
• doi:10.3103/s1066530708040017
• arXiv:2110.12122
• doi:10.1007/bf00116828
• doi:10.1145/76322.76335
• arXiv:2305.10403
• arXiv:1706.05394
• arXiv:2101.10098
• arXiv:1409.0473
• and 135 more, every one of them in the export

Works this one cites, when the source declares them as relations. What its text links to and its reference list cites is inferred, and stands under it apart.

• Retrieved from arXiv on 2026-10-09 in response to the search string “(all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"AI concepts" OR all:"types of AI" OR all:"AI fundamentals" OR all:"recognizing AI" OR all:"recognising AI" OR all:"general versus narrow AI" OR all:"narrow AI" OR all:"general AI" OR all:"machine intelligence" OR all:"AI strengths and weaknesses" OR all:"traditional software" OR all:"rule-based systems" OR all:"introduction to AI" OR all:"introduction to artificial intelligence" OR all:"artificial intelligence introduction" OR all:"AI primer" OR all:"foundations of artificial intelligence" OR all:"overview of AI" OR all:"understanding AI" OR all:"history of AI" OR all:"AI essentials" OR all:"AI terminology" OR all:"metaphors for AI" OR all:"AI fundamental concepts" OR all:"AI key concepts" OR all:"philosophy of AI" OR all:"critical AI literacy")”. arXiv served the resource and is not asserted to be its publisher or author.
• Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it.

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• URI: tag:aim-pro.eu,2026:oer/924141c704d1
• URI: https://arxiv.org/abs/2511.18517
• ARXIV: 2511.18517

This engine's identifier for the resource, the source URI, and any persistent identifier the source published.

• Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

• en

The language of the content, as the language gate read it off the converted text. The language the source declared is kept separately.

• 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 Gödelian 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.

The abstract, and the depositor's additional notes after it as a second LangString when the source has a field for them.

• cs.AI
• cs.LG

• v1

• Khanh Gia Bui — author

Role, entity and date per declared contribution. A role outside LOM's vocabulary is reported in the entry's description instead.

• URI: tag:aim-pro.eu,2026:oer/924141c704d1/record

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• AIM-PRO WP3 OER harvester (arXiv) — creator

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• LOMv1.0
• aimpro-oer-profile/1

LOMv1.0, and the profile this was built against.

• en

• text/markdown

One value per form held: the original as the source published it, and the Markdown this engine extracted.

• 231298

Bytes. The original's, because the resource is the file and not our conversion of it.

• https://arxiv.org/abs/2511.18517

Where the source serves it.

• narrative text

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• no — free to use

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• yes — attribution required (CC-BY-4.0)

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• CC-BY-4.0 · http://creativecommons.org/licenses/by/4.0/. Conditions: adaptation permitted, attribution required.

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