Beyond Reward Maximization: A Pressure-Driven Cognitive Architecture for Continual Cross-Domain Generalization and Stability

Abstract

Continual learning systems must acquire new structure without erasing prior knowledge, transfer relational schemas across domains, and regulate behavior under uncertainty. This paper presents RAVANA (Recursive Adaptive Variable Architecture for Neurocognitive Autonomy), a pressure-driven cognitive architecture that combines a Recursive Learning Model (RLM), a typed ConceptGraph, predictive-coding style local settling, sleep-time consolidation, episodic replay, and homeostatic cognitive variables. Unlike standard reward-maximizing or end-to-end backpropagation systems, RAVANA treats error, dissonance, novelty, identity stability, and memory pressure as interacting control signals. We describe the architecture in detail and report internal benchmark results from within-domain association learning, cross-domain analogy probes, lifelong streaming retention, and synthetic fairness stress tests. In these experiments, architectural fixes raised within-domain top-1 accuracy from 0% to 100%; cross-domain probes reached 14.3% top-1 and 71.4% top-10; and a combined replay, elastic-weight-consolidation, and Bayesian edge-posterior mechanism reduced measured catastrophic forgetting from 12.0% to 0.0% in a 15,000-experience stream. These results should be interpreted as prototype evidence rather than final cognitive validity: the benchmarks are synthetic, the architecture is small, and independent replication is required. Nevertheless, the system offers a concrete computational hypothesis for cognitive science: robust generalization may emerge not only from reward maximization, but from regulated tensions among prediction error, structural analogy, memory consolidation, and self-stabilizing identity constraints.