Unifying Reasoning and Planning through Energy Minimization

Generalization to harder reasoning and planning problems remains a central challenge for AI systems. Although both domains can be viewed as search problems where valid solutions are easier to verify than generate, existing methods often rely on domain-specific solvers or diffusion-based energy models tailored to individual settings. We introduce Energy Minimization Search} (EMS), a unified framework for reasoning and planning that learns a single time-invariant energy landscape. Unlike diffusion-based energy-based models (EBMs), which perform annealed inference over time-dependent landscapes, EMS performs direct gradient-based optimization on one static landscape using the same training objective and inference procedure across domains. Across reasoning tasks (graph coloring, N-Queens, 3-SAT) and planning tasks (path finding and maze solving), EMS matches or outperforms diffusion-based EBMs, domain-specific solvers, and diffusion-based planners while using substantially less inference compute.