What Should LLMs See, and How Often Should They Plan? World-State Encodings for Real-Time Games

Real-time game planning with large language models (LLMs) raises two ordered questions: how often should the planner decide, and how should the world state be encoded? We study *training-free high-level planning* in a one-versus-four asymmetric pursuit game, where a frozen LLM selects tactical tasks and targets while a fixed, pretrained RL controller executes frame-level actions. First, an interval sweep with paired output-token budgets reveals an inverted-U trend in chaser win rate under both planners: performance peaks at $4$s and declines at shorter and longer tested intervals. Shorter intervals pair with small output budgets, yielding shallow thinking and suboptimal plans, while longer ones deliver plans to a board that has moved on. Second, at this fixed interval, we compare plain text, topology, graph-text trees, and a visual minimap across seven maps and two planner models, holding the shared decision context and controller fixed. We propose *chessboard*, a local text grid whose layout mirrors map geometry and whose annotations associate entities with their states, and a JSON variant carrying identical information. Both *chessboard* variants improve the chaser win rate over the no-encoding baseline on every evaluated map under both models. The JSON variant leads on all aggregate gameplay metrics, reaching mean win rates of $62.8$% and $56.8$%, respectively $7.8$ and $7.2$ percentage points above the corresponding baselines. These findings highlight planning interval and state organization as complementary design choices for real-time LLM agents: neither more frequent replanning nor additional structure or spatial detail alone guarantees better performance.