This academic paper introduces a novel "inductive bias probe" to evaluate whether foundation models truly grasp underlying "world models" or simply excel at predictive tasks through task-specific heuristics. The authors illustrate this by showing that a model trained to predict orbital trajectories, while highly accurate, fails to apply Newtonian mechanics when adapted to related physics problems. The research extends this analysis to other domains like lattice problems and Othello, consistently revealing that these models often develop biases towards simpler, "legal next-token" patterns rather than the full, complex state of the world. Ultimately, the paper suggests that stronger inductive biases toward a known world model correlate with better performance on new, related tasks.