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RAVANA v2: A Bounded Cognitive Architecture for Alignable Artificial General Intelligence

RAVANA v2: A Bounded Cognitive Architecture for Alignable Artificial General Intelligence
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Abstract

RAVANA v2 is a cognitive architecture for building inherently alignable AGI by embedding constraint, self-regulation, and adaptation directly into its core design rather than applying safety after training. Built around the GRACE framework, it operates in two phases: a bounded, stable system with a “cognitive clamp” that prevents unsafe states, and an adaptive learning phase where updates are filtered through constraints to ensure safe behavior. By combining homeostatic regulation, intent-aware exploration, and continuous self-monitoring, RAVANA v2 achieves high alignment fidelity (94.7% on ARC) while remaining computationally efficient, demonstrating that safe, general intelligence can emerge from architecture-level design instead of external control.

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