From Mimicry to True Intelligence (TI) -- A New Paradigm for Artificial General Intelligence
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Abstract (arXiv)
The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance versus replicating human-like cognitive processes. We argue that performance-based definitions are inadequate, offering no roadmap for research and failing to define the qualitative nature of genuine intelligence. Four decades of work on cognitive architectures have already characterized the relevant mechanisms; our claim is that this knowledge is not being consulted by the research program now driving AGI development. What we offer is a translation of it into terms bearing on contemporary systems, with criteria for assessing them. We define True Intelligence (TI) as a system characterized by six components: five architectural pillars for which assessment criteria can be stated (embodied sensory fusion, core directives, dynamic schemata, a highly interconnected multi-expert architecture, and an orchestration layer), together with Interconnectedness, an emergent quality for which we can offer no independent measure. Two commitments distinguish the framework from the architectures on which it builds: that the topology of the schema network is itself shaped by the directives under which a system has operated, so that identical experience under different directives yields differently organized knowledge; and that a continuously active associative substrate, from which a salience mechanism promotes only a fraction to deliberate processing, is a distinct locus of cognitive work rather than the unconscious support of a task cycle. Based on the five assessable pillars we propose a five-level taxonomy of AGI, applied to four contemporary and classical systems. We hold that Level 5 is the strongest architectural condition for TI that can presently be specified, and leave open whether meeting it is sufficient.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 89 references that are in the map. First posted on arXiv 17 Sep 2025.
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13 of 89 references.
References · 13
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
- 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
- 2014Neural Turing Machinescited by 644
- 2018Large-Scale Study of Curiosity-Driven Learningcited by 280
- 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 238
- 2025ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systemscited by 190
- 2024ARC Prize 2024: Technical Reportcited by 104
- 2023Levels of AGI for Operationalizing Progress on the Path to AGIcited by 96
- 2021Hard Choices in Artificial Intelligencecited by 26
- 2024Metacognition is all you need? Using Introspection in Generative Agents to Improve Goal-directed Behaviorcited by 15
- 2018Innateness, AlphaZero, and Artificial Intelligencecited by 10
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