Compiled Agency: Frontier General-Purpose Coding Agents Build Winning Game Players from Bare Interaction - from Flappy Bird to StarCraft II and Civilization

Abstract:LLM agents have repeatedly struggled to convert knowledge of a game into competent play, even when researchers build the agent around the model - supplying perception, memory, skill libraries, planners, or executable-policy scaffolds. Rapid progress in coding agents raises two sharper questions: can frontier models now win games at all, and can they win them unaided, building the entire player themselves? We introduce Gauntlet, a develop-freeze-evaluate framework that ports games from small arcades to full commercial-scale titles, behind one deliberately bare contract: a general-purpose coding agent receives a game description, a raw observation/action interface, and an empty policy file - no strategy, no algorithm, no architecture. In a single autonomous session the agent experiments with the live game and engineers a standalone controller; we freeze the result and score it on held-out instances with zero model calls during play. On an unpublished procedural roguelike, held-out success spans 0-86 percent and exposes a sharp generational threshold: every observed session of a newest-generation system outperforms the best session of its predecessor. At full-game scale, a compiled raw-API controller defeats every fair StarCraft II built-in AI and two cheating variants, and single-session programs win complete games of Civilization (Freeciv) by total conquest on held-out seeds. Though at modest rates against novice AI, this is a first: no prior language-agent system had won full games of this genre standalone, without per-turn model calls and a hand-crafted tactical layer. Frontier coding agents begin to track long-horizon strategy. The frozen programs are inspectable. We call this capability compiled agency: development experience compiled into a persistent executable agent whose architecture is built by the model.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.18996 [cs.AI]

(or arXiv:2609.18996v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.18996 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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