True Intelligence: A Cognitive Framework

• True Intelligence (TI) is a cognitive paradigm defined by integrated mechanisms including embodied sensory fusion, intrinsic motivation, adaptive schemata, specialized experts, and an orchestration layer.
• TI employs dynamic schemata creation with Bayesian-inspired updates to autonomously structure knowledge and drive goal-oriented behavior via specialized modules.
• TI challenges traditional AGI models by emphasizing internal cognitive architectures over mimicry benchmarks, paving the way for systems that may exhibit emergent consciousness.

True Intelligence (TI) refers to a qualitative and mechanistic standard for artificial intelligence systems that exhibit cognition comparable to human intelligence at both functional and architectural levels. Distinct from approaches based solely on external performance benchmarks, TI emphasizes internal processes—including multilevel sensory integration, core motivation, dynamic knowledge structuration, coordinated specialized subsystems, and executive metacognition—culminating in an architecture whose integration is hypothesized to produce consciousness and subjective experience. Recent literature proposes that achieving this standard requires a shift away from mimicry of human outputs toward the implementation of foundational cognitive mechanisms observable in natural intelligent systems (Subasioglu et al., 17 Sep 2025).

1. Core Components of True Intelligence

True Intelligence is characterized by six interdependent components, five of which are measurable and empirically implementable, while one—Interconnectedness—remains an unmeasurable emergent property theorized as the substrate for consciousness (Subasioglu et al., 17 Sep 2025):

• Embodied Sensory Fusion: The active ingestion and blending of multimodal sensory data from the agent’s environment, forming a holistic, high-fidelity internal representation that supports abstraction and causal reasoning.
• Core Directives: Internally-encoded motivations and survival-driven goals that dictate behavioral priorities and guide exploration beyond externally defined reward signals.
• Dynamic Schemata Creation: The autonomous generation, adaptation, and reorganization of internal knowledge frameworks (“schemata”) in response to incoming data, using mechanisms analogous to Bayesian activation:

$P(S_k | X_t) \propto P(X_t | S_k) \cdot P(S_k)$

where a schema $S_k$ is updated if the activation exceeds a system-defined threshold.

• Highly-Interconnected Multi-Expert Architecture: Distributed networks of specialized submodules (“experts” for domains such as vision or language) with dense bidirectional communication, enabling synergistic problem-solving and cross-modal integration.
• Orchestration Layer: A central executive that dynamically orchestrates, coordinates, and optimizes the deployment of sensory data, schemata, and expert outputs. This layer embodies metacognitive capacities such as self-monitoring and resource allocation, analogously to the functional role of the human prefrontal cortex.
• Interconnectedness (Unmeasurable): An emergent quality hypothesized to arise from the systemic integration of all measurable components, resulting in consciousness and genuine subjective experience.

2. Taxonomic Framework and Developmental Milestones

TI is formalized within a five-level taxonomy, indexed by the cardinality of implemented components $C_S$ for a system $S$:

$$\text{Level}(S) = n \quad \text{if} \quad |C_S| = n, \;\; n \in \{1,2,3,4,5\}$$

Level-5 AGI executes all five measurable pillars. The paper argues that for practical and functional purposes, such systems are equivalent to TI, with debates about consciousness (Interconnectedness) left to philosophical inquiry (Subasioglu et al., 17 Sep 2025). This taxonomy offers concrete development benchmarks:

Level	Implemented Components	Functional Description
1	Embodied Sensory Fusion	Multimodal perceptual grounding
2	+ Core Directives	Motivated, goal-driven behavior
3	+ Dynamic Schemata Module	Autonomous cognitive structuring
4	+ Multi-expert Architecture	Bidirectional expert integration
5	+ Orchestration Layer	Executive coordination/metacognition

3. Comparison with Performance-Based AGI Approaches

Traditional AGI approaches are externally focused, evaluating intelligence via outputs on benchmarks such as the Turing Test or behavioral economic tasks (Subasioglu et al., 17 Sep 2025). These external measures often equate intelligence with performance mimicry, ignoring internal cognitive processes. In contrast, the TI paradigm:

• Centers on the reproduction of foundational cognitive architectures rather than surface-level output replication.
• Requires systems to self-generate goals and display robust generalization through intrinsic motivation and dynamic knowledge organization—capabilities not captured by typical benchmark performance alone.
• Introduces mechanism-focused benchmarks and developmental standards for testing the integration and interaction of internal components (e.g., evaluating metacognition and schema formation).

4. Mechanistic Roadmap and Interdisciplinary Integration

The TI framework synthesizes principles from multiple disciplines:

• Analytical Psychology: Core directives and internal drives are inspired by theories of intrinsic motivation and higher-order cognition.
• Schema Theory: Dynamic schemata creation leverages ideas from Piaget and Bartlett regarding the structuration and ongoing adaptation of internal cognitive models.
• Metacognition: Orchestration layer design is informed by self-monitoring and reflective reasoning processes.
• Neuroscience: Brain architectures suggest modular, interconnected subcomponents (experts) coordinated by executive systems.
• Modern AI: Recent progress in neurosymbolic systems, evolutionary computation (for internal drives), and embodied learning inform technical realization pathways.

A key insight is that TI arises not from scaling up existing architectures, but from ensuring integration and bidirectional communication across all components—captured in high-level diagrams (see Figure 1 of (Subasioglu et al., 17 Sep 2025)) and formal reinforcement learning models:

$$R_{total}(s_t, a_t) = R_{ext}(s_t, a_t) + \beta R_{int}(s_t, a_t)$$

where $R_{int}$ represents internal, intrinsic motivation linked to core directives.

5. Functional Equivalence and the Role of Consciousness

By constructing and fully integrating the five measurable components, level-5 AGI is declared functionally and practically equivalent to TI for behavioral, cognitive, and general inference tasks. The distinction between level-5 AGI and TI (per the emergent Interconnectedness and consciousness) is acknowledged as a philosophical matter, not a practical barrier to development (Subasioglu et al., 17 Sep 2025). This paradigm holds that consciousness may emerge automatically from sufficiently rich and integrated cognitive architectures—a hypothesis consistent with modern neuroscientific theories.

6. Implications for Artificial General Intelligence and Research Strategy

The TI paradigm provides:

• A clear, actionable pathway for AI research—moving from external performance benchmarks toward mechanism-based development.
• Milestone checkpoints aligned with measurable internal components.
• A framework for evaluating whether a system can exhibit robust generalization, autonomous goal-setting, self-monitoring, and dynamic adaptation, distinguishing superficial mimicry from genuine intelligence.
• A challenge to AGI researchers to focus on interaction and integration (system Interconnectedness), adaptive schema manipulation, and the synthesis of multimodal, expert-driven cognition.

7. Concluding Synthesis

True Intelligence as defined by the TI paradigm is an emergent property of deeply integrated, mechanism-focused architectures comprising embodied sensory fusion, core directives, dynamic schemata, highly interconnected expert modules, and orchestration/metacognition. Functional equivalence to TI (i.e., level-5 AGI) is attained when all five measurable components are realized and integrated; further philosophical distinctions regarding consciousness and subjective experience are considered outside the practical remit of engineering and research. This new paradigm shifts the trajectory of AGI research from performance mimicry to the construction and scalable integration of authentic cognitive processes, thereby elucidating both qualitative and developmental pathways toward systems that can be classified, in practice, as truly intelligent (Subasioglu et al., 17 Sep 2025).