提出新型通用智能框架，强调认知机制而非仅性能表现。

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.