From answers to questions: the Q-centric model of intelligence
Abstract While LLMs score highly on reference alignment and produce increasingly human-like responses, a gap still prevents us from reaching AGI or genuine self-awareness. Is this merely a technical problem—or a philosophical one? If ever-better “answers” in AGI are not absolute endpoints, what are...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00402-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849764591814311936 |
|---|---|
| author | Mehrdad Ghasemizadeh |
| author_facet | Mehrdad Ghasemizadeh |
| author_sort | Mehrdad Ghasemizadeh |
| collection | DOAJ |
| description | Abstract While LLMs score highly on reference alignment and produce increasingly human-like responses, a gap still prevents us from reaching AGI or genuine self-awareness. Is this merely a technical problem—or a philosophical one? If ever-better “answers” in AGI are not absolute endpoints, what are they? One way to understand “answers” is as recombinations within lived interpretation—temporary syntheses that organize experience into coherent conceptual structures. This article posits that no philosophical “answer” is a terminal truth; it is an integration of conceptual form that stabilizes meaning in the face of ambiguity. Creativity, exploration, and the very act of questioning—rather than the pursuit of definitive answers—may be the true engines of both philosophical insight and human-like intelligence. Here, we argue that genuine understanding emerges through continuous inquiry and recursive reorganization. Answers, in this view, remain provisional constructions, shaped by dissonance and forever open to revision. To ground this thesis, we embed multiple traditions as epistemic momentum vectors. Phenomenological ambiguity, ontological instability, hermeneutic dissonance, pragmatic friction, and erotetic incompleteness each introduce distinct tensions that propel the Q-Centric architecture toward recursive questioning. Their divergence is not noise, but the generative condition of meaning-making. By integrating these layered vectors, we sketch a pathway to AGI systems that do more than respond coherently; they orient themselves toward what remains unresolved. Reconceptualizing “answers” as momentary crystallizations of tension, the Q-Centric model offers a philosophical foundation for building reflective, adaptive machines that question—rather than merely conclude. |
| format | Article |
| id | doaj-art-96861495f2bc4282b0fcb3f235d368b2 |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-96861495f2bc4282b0fcb3f235d368b22025-08-20T03:05:06ZengSpringerDiscover Artificial Intelligence2731-08092025-07-015112210.1007/s44163-025-00402-wFrom answers to questions: the Q-centric model of intelligenceMehrdad GhasemizadehAbstract While LLMs score highly on reference alignment and produce increasingly human-like responses, a gap still prevents us from reaching AGI or genuine self-awareness. Is this merely a technical problem—or a philosophical one? If ever-better “answers” in AGI are not absolute endpoints, what are they? One way to understand “answers” is as recombinations within lived interpretation—temporary syntheses that organize experience into coherent conceptual structures. This article posits that no philosophical “answer” is a terminal truth; it is an integration of conceptual form that stabilizes meaning in the face of ambiguity. Creativity, exploration, and the very act of questioning—rather than the pursuit of definitive answers—may be the true engines of both philosophical insight and human-like intelligence. Here, we argue that genuine understanding emerges through continuous inquiry and recursive reorganization. Answers, in this view, remain provisional constructions, shaped by dissonance and forever open to revision. To ground this thesis, we embed multiple traditions as epistemic momentum vectors. Phenomenological ambiguity, ontological instability, hermeneutic dissonance, pragmatic friction, and erotetic incompleteness each introduce distinct tensions that propel the Q-Centric architecture toward recursive questioning. Their divergence is not noise, but the generative condition of meaning-making. By integrating these layered vectors, we sketch a pathway to AGI systems that do more than respond coherently; they orient themselves toward what remains unresolved. Reconceptualizing “answers” as momentary crystallizations of tension, the Q-Centric model offers a philosophical foundation for building reflective, adaptive machines that question—rather than merely conclude.https://doi.org/10.1007/s44163-025-00402-wConsciousnessQuestion-centric cognitionEpistemic architectureEmbodied intelligenceIntelligent systems designAGI |
| spellingShingle | Mehrdad Ghasemizadeh From answers to questions: the Q-centric model of intelligence Discover Artificial Intelligence Consciousness Question-centric cognition Epistemic architecture Embodied intelligence Intelligent systems design AGI |
| title | From answers to questions: the Q-centric model of intelligence |
| title_full | From answers to questions: the Q-centric model of intelligence |
| title_fullStr | From answers to questions: the Q-centric model of intelligence |
| title_full_unstemmed | From answers to questions: the Q-centric model of intelligence |
| title_short | From answers to questions: the Q-centric model of intelligence |
| title_sort | from answers to questions the q centric model of intelligence |
| topic | Consciousness Question-centric cognition Epistemic architecture Embodied intelligence Intelligent systems design AGI |
| url | https://doi.org/10.1007/s44163-025-00402-w |
| work_keys_str_mv | AT mehrdadghasemizadeh fromanswerstoquestionstheqcentricmodelofintelligence |