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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2512.00683 |
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| _version_ | 1866908681927393280 |
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| author | Yonggang, Wu |
| author_facet | Yonggang, Wu |
| contents | The development of large language models (LLMs) is limited by a lack of explainability, the absence of a unifying theory, and prohibitive operational costs. We propose a neuro-theoretical framework for the emergence of intelligence in systems that is both functionally robust and biologically plausible. The model provides theoretical insights into cognitive processes such as decision-making and problem solving, and a computationally efficient approach for the creation of explainable and generalizable artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model of human cognition Yonggang, Wu Artificial Intelligence The development of large language models (LLMs) is limited by a lack of explainability, the absence of a unifying theory, and prohibitive operational costs. We propose a neuro-theoretical framework for the emergence of intelligence in systems that is both functionally robust and biologically plausible. The model provides theoretical insights into cognitive processes such as decision-making and problem solving, and a computationally efficient approach for the creation of explainable and generalizable artificial intelligence. |
| title | Model of human cognition |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.00683 |