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This paper evaluates whether large language models can function as interpretable controllers for dynamical systems, specifically a thermal environment. It finds that high-complexity models like Qwen-3 14B and GPT-4o achieve accurate control and coherent reasoning, while smaller models struggle, highlighting opportunities for hybrid model-based and language-driven control strategies.
Introduces DWM, a framework that decomposes latent world model transitions into action-driven and action-invariant (world effect) components, improving planning success on benchmarks with persistent world effects.
OpenAI proposes POLO (Plan Online, Learn Offline), a framework combining model-based control with value function learning and coordinated exploration to enable efficient learning on complex control tasks like humanoid locomotion and dexterous manipulation with minimal real-world experience.
OpenAI introduces a method for learning complex nonlinear system dynamics using deep generative models over temporal segments, enabling stable long-horizon predictions and differentiable trajectory optimization for model-based control.