Context-Aware LLM Agents as World-to-Game Bridges for Solving Open Games

Game theory underpins many high-stakes applications—security, defense, resource allocation, auctions, and negotiation—yet practical deployment is blocked by a fundamental tension: models faithful enough to capture reality are typically intractable, while models simple enough to solve discard the information that actually matters. In real deployments the game is also dynamic and open: its parameters change over time, and the decisive information arrives as unstructured, semantic, and drifting context (traffic reports, road closures, observed trajectories, market signals). Existing solvers—equilibrium algorithms, deep RL, or pure LLM-generated policies—cannot consume such context, because they assume a fully specified, fixed, numeric game. We introduce the Context-Aware Game-Theoretic Agent (CAGTA), a general framework that resolves this tension. An LLM acts solely as a world-to-game bridge: it interprets the unstructured context stream and maps it into the numeric parameters of a game that a solver can handle, thereby deciding what to be robust against, while the solver decides how to be optimal. Experiments on urban network security games demonstrate substantial gains in scalability and generalization over strong context-blind baselines.