Implicit Semantic Control Manifolds for Learning-Enabled Multi-UAV Coordination

We present an implicit semantic control representation for learning-enabled autonomous flight in which biologically inspired coordination behaviors are embedded within a large language model (LLM) and constrained by a six parameter motion–LED control manifold derived from nonlinear six-degree-of freedom quadrotor dynamics. The framework encodes behavioral constraints as an implicit generative representation that integrates model-based flight physics with data-driven policy learning, enabling decentralized aerial agents to generate dynamically feasible actions and semantically consistent visual communication under radio-frequency-degraded conditions. A high-capacity teacher LLM is trained within this manifold and distilled and quantized into a compact stu dent model suitable for edge deployment on lightweight aerial platforms. The teacher, student, and classical regression baselines (multilayer perceptron and k nearest neighbor) are evaluated in a closed-loop target search simulation of 200 trials with both nominal on-manifold inputs and corrupted off-manifold pertur bations. LLM-based policies achieve higher semantic robustness (86.0% teacher, 83.2% student) than kNN (71.0%) and MLP (65.0%) and degrade more grace fully under severe corruption. The teacher and student also reduce final position error (8.37 m and 10.46 m) relative to kNN (14.02 m) and MLP (15.54 m), while distillation reduces mean inference latency from 5.24 s to 2.81 s.