When to Shape a Teammate: Inducing Decision-Relevant Belief Changes for Better Cooperative Futures

Theory of Mind studies how agents infer others' hidden states from behavior, while legibility studies how behavior can shape such inference. In cooperative tasks, however, interpretation itself is not the objective: what matters is how it changes a teammate's behavior and future team outcomes. This raises a central question: when should an agent intentionally sacrifice its own immediate optimality to shape another agent's future behavior? A large belief change may have little effect when the teammate's decision remains unchanged, whereas a small change near a decision boundary can redirect its behavior and lead to a different cooperative future. We develop a closed-loop planning approach that evaluates candidate actions through the teammate responses and future joint outcomes they induce. The agent predicts how its behavior changes the teammate's belief and subsequent response, compares the resulting joint future with task-direct behavior, executes one action, and replans from the realized joint state. Across 400 held-out GridWorld scenes and 87 PARTNR-derived household instances, our approach reaches 97.0% and 62.1% complementary-task accuracy, compared with 95.5%/96.25% and 36.8%/46.0% for open- and closed-loop belief-based cooperative planning baselines.