Classical robot control, Finite State Machines and Behavior Trees (BTs), is deterministic and inspectable but fixed at design time: it fails on situations its designers did not anticipate. Foundation models can synthesize new control logic at runtime, at a cost in latency and non-determinism hard to accept in safety-critical aerial systems. Existing LLM-driven approaches keep the model in the loop permanently and discard what it produces. We propose Self-Crystallizing Behavior Trees (SCBT), in which the foundation model is a compiler rather than a runtime. When a BT node fails, an LLM proposes a minimal, mission-agnostic subtree patch; CMA-ES tunes its parameters in simulation; a contextual bandit decides whether a stored patch or a new LLM call is worth its cost; and a three-stage admission pipeline (static check, invariant-monitored replays, an empirical margin over the unpatched tree) admits only patches that demonstrably recover. Admitted patches are stored in a fleet library and, once the LLM call rate decays, crystallized into a deterministic BT that no human wrote. We implement SCBT on Microsoft Project AirSim with a schema-constrained LLM pipeline (Gemini 3.1 Pro) and a lockstep simulator. Over three seeds of a 20-episode learning phase with wind-gust and battery failures, SCBT used $11\pm1.4$ LLM calls against 26 for whole-tree regeneration and 22 without a library, with 0.33 calls per episode in the second half. The crystallized trees, 28 and 33 nodes with two patches each, reached $97\pm2\%$ mission success with no learning running, against 12\% for the unpatched tree and 98\% for a hand-coded expert tree, and reproduce decision traces exactly when re-ticked offline. Two negative results: replay admission rejected useful wind recoveries by attributing wind-induced geofence excursions to the patch, and a static-only ablation generalized better to held-out failures.

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