Systems Explaining Systems: A Framework for Intelligence and Consciousness

TL;DR

This paper introduces a conceptual framework where intelligence and consciousness arise from relational structures and recursive architectures, emphasizing context enrichment and dynamic interpretation over prediction for more human-like AI.

Contribution

It presents a novel systems-based framework for understanding intelligence and consciousness as emergent from relational and recursive structures, shifting focus from prediction to contextual interpretation.

Findings

Intelligence is based on forming and integrating causal relational connections.

Consciousness emerges from recursive architectures interpreting relational patterns.

Recursive multi-system architectures are proposed as necessary for advanced AI.

Abstract

This paper proposes a conceptual framework in which intelligence and consciousness emerge from relational structure rather than from prediction or domain-specific mechanisms. Intelligence is defined as the capacity to form and integrate causal connections between signals, actions, and internal states. Through context enrichment, systems interpret incoming information using learned relational structure that provides essential context in an efficient representation that the raw input itself does not contain, enabling efficient processing under metabolic constraints. Building on this foundation, we introduce the systems-explaining-systems principle, where consciousness emerges when recursive architectures allow higher-order systems to learn and interpret the relational patterns of lower-order systems across time. These interpretations are integrated into a dynamically stabilized meta-state and fed back through context enrichment, transforming internal models from representations of the external world into models of the system's own cognitive processes. The framework reframes predictive processing as an emergent consequence of contextual interpretation rather than explicit forecasting and suggests that recursive multi-system architectures may be necessary for more human-like artificial intelligence.