Researchers propose using active inference to help AI agents acquire the right context efficiently. This involves balancing between making default assumptions and spending resources on clarifying questions or actions. The framework is model-agnostic and can be applied to various tasks, including question asking, clarification before generation, and automated prompt optimization. --- Why it matters: This work matters because it provides a principled approach to context acquisition in AI agents, which is crucial for improving their performance and efficiency in real-world applications. Source: https://arxiv.org/abs/2608.19202