Magic Tower AI Lab · Thesis
Master's thesis, M.Sc. Data Science · Leuphana University Lüneburg

From Reactive Control to Holistic Planning: Evolutionary Prompt Optimization for Hybrid Deterministic-Neural Agents in Long-Horizon Environments

King Wai Siu · supervised by Prof. Dr. Ricardo Usbeck and Prof. Dr. Burkhardt Funk

A language model plays Magic Tower, a deterministic game decided by the order of fights, doors and items. A deterministic engine lists the legal actions with their exact costs, the model chooses one, and an exact solver knows each floor's optimum. This site holds the evidence: every result file, every episode with the model's reasoning at each turn, and the route from the first experiments to the final configuration.

The thesis in three minutes

Captions are in the picture. The interactive version pauses at each demo so you can try it, and links the captions file and the voice-over script.

Main findings

15 / 15simple-floor episodes won by Qwen3.5-397B with the engine's annotations, each at 2 HP (optimum 13)the re-run cell
0/5 → 10/10after two annotation lines that state both sides of a trade-off; same model, same promptthe repair sequence
MT7 → MT18highest tower floor across three observation designs; a resumed run reached the normal ending on MT21the tower runs
1 / 70simple-floor wins with committed plans, against 14 of 20 for reactive playthe planning study

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