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Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

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…

Licence: OPEN CC-BY-4.0
Authors: Khanh Gia Bui
Published: 2025-11-23 · arXiv
Language: en
Length: 36705 words
Type: narrative text

Cites 147 works

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Extracted text · References Page 3 of 3 in this section · Words 28,536–29,232 of 33,276

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techniques for neural network optimization. pp. 3–11. External Links: Document Cited by: §3.2.

S. Wang, M. Hu, Q. Li, M. Safari, and X. Yang Capabilities of gpt-5 on multimodal medical reasoning. External Links: 2508.08224, Link Cited by: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35, pp. 24824–24837. Note: NeurIPS 2022. Demonstrated how step-by-step reasoning improves LLM performance External Links: Link Cited by: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI.
J. Wei et al. From ai for science to agentic science: a survey on autonomous scientific discovery. arXiv preprint arXiv:2508.14111. External Links: Link Cited by: §2.
G. Weiss, Y. Goldberg, and E. Yahav On the practical computational power of finite precision rnns for language recognition. In Proceedings of ACL (short papers) / arXiv, Note: arXiv:1805.04908 External Links: Link Cited by: §2.3.3.
T. Winograd Understanding natural language. Academic Press, New York. Note: SHRDLU project — language understanding in a blocks world; core early symbolic NLP system External Links: Link Cited by: §2.3.2.
D. H. Wolpert and W. G. Macready No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 (1), pp. 67–82. External Links: Document, Link Cited by: §2.3.3.
D. H. Wolpert The lack of a priori distinctions between learning algorithms. Neural Computation 8 (7), pp. 1341–1390. External Links: Document, Link Cited by: §2.3.2.
R. Worden A unified theory of language. External Links: 2508.20109, Link Cited by: §1.4.
S. J. Wright Coordinate descent algorithms. Mathematical Programming 151 (1), pp. 3–34. External Links: Document Cited by: §2.3.3.
K. Xu, M. Zhang, J. Li, S. S. Du, K. Kawaguchi, and S. Jegelka How neural networks extrapolate: from feedforward to graph neural networks. arXiv preprint arXiv:2009.11848. Cited by: §2.3.
E. Yudkowsky and N. Soares If anyone builds it, everyone dies: why superhuman ai would kill us all. Hachette Book Group. External Links: ISBN 9780316595643 Cited by: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI.
A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola Dive into deep learning. External Links: 2106.11342, Link Cited by: Definition 2.2.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals Understanding deep learning requires rethinking generalization. External Links: 1611.03530, Link Cited by: §2.3.3.
H. Zhang, C. H. Yang, H. Zenil, N. A. Kiani, Y. Shen, and J. N. Tegner Evolving neural networks through a reverse encoding tree. In arXiv preprint, Note: arXiv:2002.00539 External Links: Link Cited by: §3.2.
Y. Zhang et al. LLLMs: a data-driven survey of evolving research on limitations of large language models. arXiv preprint arXiv:2505.19240. Note: Semi-automated review of 14,648 papers on LLM limitations from 2022-2024, covering reasoning failures, hallucinations, and multilingual capabilities External Links: Link Cited by: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI.
W. X. Zhao et al. Large language models: a survey. arXiv preprint arXiv:2402.06196. Note: Reviews prominent LLM families (GPT, LLaMA, PaLM) and discusses techniques for building and augmenting LLMs External Links: Link Cited by: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223. Note: Comprehensive survey covering pre-training, adaptation, utilization, and evaluation of LLMs. Updated through March 2025 External Links: Link Cited by: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI.
Y. Z. Zhijian Xu, M. Patwardhan, L. Vig, and A. Cohan Can llms identify critical limitations within scientific research? a systematic evaluation on ai research papers. arXiv preprint arXiv:2507.02694. Note: Presents LimitGen benchmark and taxonomy of limitation types in AI research, evaluating LLMs’ meta-cognitive capabilities External Links: Link Cited by: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI.
E. Øygarden What is intelligence? a proposed framework of four different concepts of intelligence. Master’s thesis, University of Agder. External Links: Link Cited by: §1.

Metadata record

One description, two standard projections

Built from what the sources declared and what the gates observed. Nothing absent has been filled in here. 1 value read out of the text by the enrichment rules and 147 links to or from other resources — a lab's files, the pages it links, the works it cites stand under their elements, marked inferred, and are kept apart in the exports.

Valid

Profile: aimpro-oer-profile/1 built 2026-10-09
Content language: en declared English

Export Dublin Core LOM Internal record

Show empty elements

DCMI Metadata Terms. Dublin Core has no element that separates the original file from the text extracted out of it, and none for LOM's educational characterisation. Both survive here as provenance statements and in the record itself, not in the projection. the standard ↗

The groups below are this library's, for reading. DCMI Terms itself has no categories; each term keeps its standard name.

Identity 3/3

Identifier dcterms:identifier this engine resource_id source resource/arxiv_id: 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.
Title dcterms:title source resource/title: Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

The title of this resource, not of the record or repository it came from.
Type dcterms:type source resource: dcmitype:Text
preprint

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

Responsibility 1/3

Creator dcterms:creator

source resource/authors/0

Khanh Gia Bui

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

Contributor dcterms:contributor

source resource/authors/0

none found — the source did not declare it

Everyone else named, with the role as declared and the scope they were declared at.

Publisher dcterms:publisher

none found — the source did not declare it

Only when the source names a publisher of the work. The platform it was collected from is recorded as provenance instead.

Content 3/5

Description dcterms:description

source resource/summary

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.

Subject dcterms:subject

source resource/categories/0

cs.AI (arxiv)
cs.LG (arxiv)

Declared keywords and taxonomy terms, each carrying its scheme.

Language dcterms:language

language gate read the declared field

The language of the content as the language gate read it.

Audience dcterms:audience

mapped learning_resource_type/preprint

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Education level dcterms:educationLevel

mapped learning_resource_type/preprint

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Imported when the source declares an educational context.

Rights 3/4

Licence dcterms:license

licence gate arXiv:arXiv/arXiv:license

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

The licence document, as a URI where the gate resolved one.

Rights dcterms:rights

licence gate arXiv:arXiv/arXiv:license

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

The verdict and the conditions, in words.

Rights holder dcterms:rightsHolder

none found — the source did not declare it

Only when named at source.

Access rights dcterms:accessRights

source resource

open

The access condition the source declared. Not the licence.

Form and dates 3/10

Format dcterms:format

conversion latexml-html

text/markdown

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

Extent dcterms:extent

conversion latexml-html

231298 bytes (extracted text)

Sizes in bytes, each saying which form it measures.

Issued dcterms:issued

source resource

2025-11-23

When the resource was published. Not when it was collected.

Modified dcterms:modified

source resource

none found — the source did not declare it

When the source's own record was last changed.

Created dcterms:created

source resource

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Accepted dcterms:dateAccepted

source resource

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When a publisher accepted the work, where the source records it.

Submitted dcterms:dateSubmitted

source resource

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When the work was submitted, where the source records it.

Available dcterms:available

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none found — the source did not declare it

Valid dcterms:valid

source resource

none found — the source did not declare it

Bibliographic citation dcterms:bibliographicCitation

none found — the source did not declare it

Where the work was published, in the source's own words: a journal with its volume and pages, a conference, an imprint.

Relations and custody 1/9

Is part of dcterms:isPartOf

none found — the source did not declare it

The repository, book or record this resource was found inside.

Has part dcterms:hasPart

none found — the source did not declare it

What this resource is made of, when the source lists its parts.

Is version of dcterms:isVersionOf

none found — the source did not declare it

Has version dcterms:hasVersion

none found — the source did not declare it

References dcterms:references

inferred read its reference list, line 429 citation

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.

Is referenced by dcterms:isReferencedBy

none found — the source did not declare it

What links to or cites this one, when a source declares it; inferred from the texts held otherwise, and set apart.

Requires dcterms:requires

none found — the source did not declare it

What this resource needs in order to be used, when a source declares it. The files a lab works on are inferred, and stand under it apart.

Is required by dcterms:isRequiredBy

none found — the source did not declare it

What needs this resource, when a source declares it. The lab a component belongs to is inferred, and stands under it apart.

Provenance dcterms:provenance

this engine source
conversion latexml-html

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.

Where it was collected from, what was converted, and what container it came out of — the custody statements that would otherwise be mistaken for authorship.

IEEE 1484.12.1 Learning Object Metadata. LOM has no element for an SPDX identifier or a licence URI, so both are written into 6.3 Rights.Description. Flattening this record into simple Dublin Core would lose more again, which is why the two projections exist side by side rather than one being generated from the other. the standard ↗

1 General 5/8

Identifier 1.1

this engine resource_id
source resource/arxiv_id

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.

Title 1.2

source resource/title

Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI

Language 1.3

language gate read the declared field

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

Description 1.4

source resource/summary

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.

Keyword 1.5

source resource/categories/0

cs.AI
cs.LG

Coverage 1.6

not collected — this library does not fill it

The time, place or culture the resource applies to. No source declares it.

Structure 1.7

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

Aggregation level 1.8

not collected — this library does not fill it

Not declared by any of the three sources, and not estimated here.

2 Life cycle 2/3

Version 2.1

source resource

Status 2.2

none found — the source did not declare it

Draft, final, revised or unavailable, and only when the source says so. A first version is not thereby final.

Contribute 2.3

source resource/authors/0

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.

3 Meta-metadata 4/4

Identifier 3.1 this engine resource_id: URI: tag:aim-pro.eu,2026:oer/924141c704d1/record

The identifier of this metadata record — the resource's own, with /record after it, because the record is a description of the resource and not the resource.
Contribute 3.2 this engine source: AIM-PRO WP3 OER harvester (arXiv) — creator

Who generated this record and when — a statement about the record, not about the resource.
Metadata schema 3.3: LOMv1.0
aimpro-oer-profile/1

LOMv1.0, and the profile this was built against.
Language 3.4 language gate read the declared field: en

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Format 4.1

conversion latexml-html

text/markdown

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

Size 4.2

conversion latexml-html

231298

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

Location 4.3

this engine resource_id

https://arxiv.org/abs/2511.18517

Where the source serves it.

Requirement 4.4

not collected — this library does not fill it

Software or hardware needed to use it. No source declares it.

Installation remarks 4.5

not collected — this library does not fill it

No source declares it.

Other platform requirements 4.6

not collected — this library does not fill it

No source declares it.

Duration 4.7

not collected — this library does not fill it

Playing time, for audio and video. The corpus holds neither.

5 Educational 1/11

Interactivity type 5.1

not collected — this library does not fill it

Active, expositive or mixed. A judgement about how the resource is used; no source declares it.

Learning resource type 5.2

source resource
mapped learning_resource_type/preprint

narrative text

Only from a declaration that maps onto LOM's vocabulary. A file format is not a declaration.

Interactivity level 5.3

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Semantic density 5.4

not collected — this library does not fill it

No source declares it, and nothing here reads the resource to judge it.

Intended end user role 5.5

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Context 5.6

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared.

Typical age range 5.7

not collected — this library does not fill it

No source declares it.

Difficulty 5.8

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared and never estimated.

Typical learning time 5.9

mapped learning_resource_type/preprint

none found — the source did not declare it

Imported when declared. Never computed from a word count.

Description 5.10

mapped learning_resource_type/preprint

none found — the source did not declare it

An explicit statement about educational use, when the source makes one.

Language 5.11

not collected — this library does not fill it

The language of the intended learner. Not assumed to be the content's: a lesson in English may be written for learners of it.

6 Rights 3/3

Cost 6.1 licence gate arXiv:arXiv/arXiv:license: no — free to use

No: the licence gate refuses anything it cannot establish open reuse terms for, before the bytes are requested.
Copyright and other restrictions 6.2 licence gate arXiv:arXiv/arXiv:license: yes — attribution required (CC-BY-4.0)

Yes unless the licence reserves nothing — attribution is a restriction. The conditions after the dash are the licence gate's reading; the export carries LOM's bare term.
Description 6.3 licence gate arXiv:arXiv/arXiv:license: CC-BY-4.0 · http://creativecommons.org/licenses/by/4.0/. Conditions: adaptation permitted, attribution required.

The SPDX id, the licence URI and the conditions. LOM has no element for any of the three, so this is where they survive.

7 Relation 0/2

Kind 7.1: none found — the source did not declare it
Resource 7.2 inferred read its reference list, line 429 citation: references: arXiv:2403.15529
references: arXiv:2102.09798
references: arXiv:2507.07156
references: arXiv:2006.05205
references: doi:10.3103/s1066530708040017
references: arXiv:2110.12122
references: doi:10.1007/bf00116828
references: doi:10.1145/76322.76335
references: arXiv:2305.10403
references: arXiv:1706.05394
references: arXiv:2101.10098
references: arXiv:1409.0473
and 135 more, every one of them in the export

The container a file was found inside, and the Markdown extracted from the original. What a lab requires, and the lab a component belongs to, are inferred and stand apart.

8 Annotation 0/3

Entity 8.1

not collected — this library does not fill it

Comments on the resource's educational use, by whoever made them. The platform's review grades competencies, which are classification (9), and writes no comment here.

Date 8.2

not collected — this library does not fill it

Description 8.3

not collected — this library does not fill it

9 Classification 0/4

Purpose 9.1

not collected — this library does not fill it

Empty in the record: no source declares a competency. The alignment reads the resource for them and stands beside the record, never in it, and a taxon path derived from the search string that found it would be a claim about the query.

Taxon path 9.2

not collected — this library does not fill it

Where the competency framework goes. Empty in the record for the reason above.

Description 9.3

not collected — this library does not fill it

Keyword 9.4

not collected — this library does not fill it

What could not be established3

Where the source's metadata could not be carried over as it was — missing, contradictory, with no matching term in the standard, restructured, or taken from the repository — and what was done instead. Without these notes, an empty element would look like something the harvester missed.

Status	Field	Why
Not available	`rights_holder`	no rights holder is named at source; the licence is recorded without one rather than attributed to the platform that served it
Not available	`publisher`	the source named no publisher of the work; where it was collected from is recorded as collection provenance instead, which is a different claim
Not available	`educational`	the source declared no educational metadata — no resource type, audience, context, difficulty or learning time. Nothing here estimates them