Apple and Oxford just made AI 6.5x better at problem-solving. The secret: it teaches AI agents to ask perfect questions. This rockets success rates from 14% to 91%. No need for fine-tuning or retraining. It runs on current models. Here's how it works: It's a strategic loop designed for multi-turn conversations. At every step, the agent works to find the shortest path to the right answer. Hypothesize: The agent creates an internal list of all possible solutions to the problem. Score Questions: It simulates asking various questions and scores each one on "Expected Information Gain" (EIG). This number represents how much a question is mathematically likely to shrink the list of possibilities. Ask the Best Question: It asks the user only the single, highest-scoring question. Update & Repeat: Based on the answer, it filters its list of hypotheses, getting smarter with each interaction, and then begins the loop again for the next turn. Why this matters for your AI strategy: This marks a shift from building passive "oracles" to proactive, question-asking agents Business Leaders: A 6.5x multiplier on task success is a lever for efficiency. This translates to fewer failed customer interactions, faster diagnostics, and more accurate personalization, a clear ROI on smarter AI. Practitioners: This is a deployment-time framework, not a new model. You can build this agent on top of existing LLMs today. It provides a principled way to overcome common multi-turn issues like inconsistency and context loss without fine-tuning or retraining. Researchers: This paper is a victory for information theory. It proves that a full EIG calculation is superior to heuristics like predictive entropy. It sets a new standard for how to build intelligent information-seeking agents.