[2603.20333] Bounded Coupled AI Learning Dynamics in Tri-Hierarchical Drone Swarms

Computer Science > Machine Learning arXiv:2603.20333 (cs) [Submitted on 20 Mar 2026] Title:Bounded Coupled AI Learning Dynamics in Tri-Hierarchical Drone Swarms Authors:Oleksii Bychkov View a PDF of the paper titled Bounded Coupled AI Learning Dynamics in Tri-Hierarchical Drone Swarms, by Oleksii Bychkov View PDF Abstract:Modern autonomous multi-agent systems combine heterogeneous learning mechanisms operating at different timescales. An open question remains: can one formally guarantee that coupled dynamics of such mechanisms stay within the admissible operational regime? This paper studies a tri-hierarchical swarm learning system where three mechanisms act simultaneously: (1) local Hebbian online learning at individual agent level (fast timescale, 10-100 ms); (2) multi-agent reinforcement learning (MARL) for tactical group coordination (medium timescale, 1-10 s); (3) meta-learning (MAML) for strategic adaptation (slow timescale, 10-100 s). Four results are established. The Bounded Total Error Theorem shows that under contractual constraints on learning rates, Lipschitz continuity of inter-level mappings, and weight stabilization, total suboptimality admits a component-wise upper bound uniform in time. The Bounded Representation Drift Theorem gives a worst-case estimate of how Hebbian updates affect coordination-level embeddings during one MARL cycle. The Meta-Level Compatibility Theorem provides sufficient conditions under which strategic adaptation preserves lower-level...