A cognitive synergetic hierarchical framework for UAV swarm combat via speculative inference and role-decoupled reinforcement learning.

In the high-stakes arena of aerial combat-a domain defined by extreme dynamics and unforgiving physical constraints-UAV swarms are currently squeezed between two extremes: the "tactical short-sightedness" of Multi-Agent Reinforcement Learning (MARL) and the "inference lag" of Large Language Models (LLMs). While MARL struggles to internalize the complex maneuverability priors required for expert flight, LLMs are simply too heavy to meet millisecond-level control demands. We bridge this gap by introducing a cognitive synergetic hierarchical framework that decouples strategic reasoning from tactical execution. Our architecture splits the workload between a "Strategic Brain" and a "Tactical Torso." For the Brain, we utilize a synergy between DeepSeek-R1 (70B) and its 7B distilled counterpart to create a collaborative inference engine. By capitalizing on the inherent sparsity of tactical logic in air combat, we implemented a speculative decoding mechanism that achieves an effective boost in decision throughput while maintaining the deep logic of the full 70B model. For the Torso, we developed an enhanced MAPPO algorithm that processes relative pose graphs via graph attention. By integrating a KL-divergence constraint into the loss function, we essentially force agents with different payloads-like scouts and attackers-to evolve specialized tactical personalities within a shared latent space. Experimental results using the JSBSim high-fidelity 6-DOF engine demonstrate that the swarm does more than just improve its exchange ratio. Further t-SNE manifold analysis and Chain-of-Thought visualizations confirm that our architecture successfully aligns symbolic intent with raw physical control. Most notably, through our "decision-reflection-evolution" loop, the system proved it could diagnose its own failures, and iteratively refine its own tactical instructions.