A memory system that learns from its own behavior. Unlike current AI which forgets everything between interactions, RMA enables persistent, evolving memory that develops retrieval intuition through use. The implications for long-horizon reasoning and continuous learning are structural, not incremental.
A fundamentally new approach to temporal reasoning in AI. Current systems understand sequence position but have no native concept of time, causality, or temporal distance. TIG treats these as first-class computational primitives, enabling causal reasoning that current architectures cannot achieve.
AI that builds internal models of reality as continuous representations. Rather than predicting the next token, WMS-equipped systems simulate outcomes across physical, social, and strategic domains. The system doesn't guess what comes next — it understands why.
The decomposition of intelligence into its fundamental, irreducible cognitive modes — analogous to how eigenvalues reveal the essential modes of a mathematical system. Current AI operates in a narrow band of cognition. Eigenintelligence reveals the full spectrum and enables dynamic mode-switching during reasoning.
Multi-axis task classification that routes cognitive workloads to optimal processing strategies. Instead of one model handling everything the same way, CRP analyzes the nature of each reasoning step and activates the appropriate cognitive pathway. The result: dramatically higher intelligence per unit of compute.
The vast majority of intelligence processing that occurs below observable outputs. Analogous to dark matter in physics — you can't see it directly, but it shapes everything. Current AI exposes all its computation as tokens. IQIUU systems maintain deep processing layers that inform decisions without being expressible in language.
A fluid intelligence state between structured reasoning and chaos. Like physical plasma, Cognitive Plasma is an energized, adaptive medium where novel connections form spontaneously. This is where creativity lives — and where current rigid architectures cannot reach.
Multi-scale recursive lattice connecting micro-cognition to macro-reasoning. Inspired by Integrated Information Theory, Phi-Mesh enables irreducible internal representations — the architectural foundation for AI that doesn't just process information, but develops genuine understanding.
A new framework for measuring real intelligence — not benchmark scores, but genuine understanding per unit of computation. Current models are profoundly inefficient. IDM redefines what "better" means in AI: less compute, deeper understanding. This is the metric that matters.
Systems that develop their own sub-goals from high-level operator intent. EGA bridges the gap between "do X" and the thousand micro-decisions required to achieve X — enabling goal decomposition, priority negotiation, and autonomous re-planning when reality diverges from expectation.
The mathematically defined threshold where a system's Intelligence Density Metric exceeds human cognitive baseline across all eigenmodes simultaneously. Not a vague "superintelligence" — a measurable milestone with defined criteria. IQIUU doesn't guess when it arrives. We measure the distance.