Subsumption Pattern Learning: A Formal Framework for Self-Distilling Swarm Intelligence Through Shared Collective Memory

We introduce Subsumption Pattern Learning (SPL), a hierarchical multi-agent framework that transforms collections of autonomous AI agents into a self-distilling swarm intelligence through shared collective memory. SPL adapts Brooks’ subsumption architecture from behavioral robotics to foundation model economics, implementing a formally-defined three-layer hierarchy (Reactive, Tactical, Deliberative) where learned patterns are distilled into a centralized Shared State via explicit inhibition signals. We provide a complete mathematical formalization of the pattern distillation process, defining state transitions from deliberative reasoning to tactical reflexes through confidence-bounded suppression logic. Our framework unifies three previously disparate research streams: subsumption control from robotics, social learning theory from cognitive science, and swarm intelligence from distributed systems. We present rigorous empirical evaluation on a benchmark of 100,000 heterogeneous enterprise tasks, demonstrating 5–15× cost reduction per agent with an additional 40% reduction in foundation model escalations across coordinated multi-agent networks. Ablation studies confirm that cost savings preserve accuracy within 1.3% of baseline. We formalize the intelligence compounding phenomenon, proving that collective competency grows logarithmically with processed requests under mild assumptions. SPL provides a principled path toward AI systems that grow more intelligent with every transaction while maintaining robustness through decentralized resilience.