Reflective Agent Architecture
Enables AI systems to detect confusion, search for alternative conceptual framings, and achieve insight-like problem solving through a metacognitive architecture.
About
The Reflective Agent Architecture (RAA) is a research prototype that integrates modern associative memory, metacognitive monitoring, and dynamic goal reframing to empower AI systems with insight-like problem-solving capabilities. It synthesizes Modern Hopfield Networks for exponential-capacity knowledge storage, entropy-based mechanisms for detecting model uncertainty, and dynamic attention to adapt to varying levels of confusion, allowing the system to refactor its goals and discover novel solutions.
Key Features
- Utility-guided architecture with knowledge compression for reusable patterns
- Explicit metacognitive loop for AI insight generation
- Entropy-triggered associative search for dynamic goal reframing
- Modern Hopfield Networks for high-capacity associative memory
- Adaptive beta modulation for dynamic control of attention sharpness
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Use Cases
- Solving complex problems requiring deep insight and conceptual reframing
- Developing self-optimizing AI architectures through metacognitive analysis
- Generating novel connections and unified theories in multi-domain philosophical or scientific queries
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