这篇论文提出了AI智能体上下文获取的新方法，值得一读。它通过最优提问，提高了智能体的性能和效率，与现有方法相比有显著不同。
本文探讨了AI智能体当前存在的问题，如幻觉、成本效率低下等，提出了解决方案。通过将上下文获取视为主动推理，提出了一种名为“最优提问”的方法，并在25至300个候选者的二进制和多路任务上进行了基准测试。
What a fascinating paper on AI agents. A lot of the issues we see with AI agents today revolve arou...
What a fascinating paper on AI agents. A lot of the issues we see with AI agents today revolve around wrong assumptions the LLMs make. This leads to problems like hallucination, cost inefficiencies, unreliable tool calls and much more. I think if we can solve this problem, even current LLMs would significantly improve in terms of performance and efficiency. The problem is that context acquisition is treated as afterthought, but it shouldn't be that way. Users tend to leave out constraints when prompting. So the agent agent needs to guess the default, or spend tokens on a clarifying question, a retrieval call, a tool call, or a prompt trial. This new work gives this problem an objective function. Context acquisition becomes active inference over a latent task state. An inner step updates beliefs, and an outer step picks the next context action, task action, or stop action to minimize expected free energy under cost. In deterministic settings the epistemic term reduces to expected information gain, optionally normalized by token cost. That is directly implementable today as a scoring rule. They coin it as Optimal Question Asking, with exact posteriors and a dynamic programming oracle, then benchmark frontier models on binary and multiway tasks from 25 to 300 candidates. So you can measure the gap between your agent and the true optimum. Paper: arxiv.org/abs/2608.19202 Track more trending AI papers in our academy: academy.dair.ai 💬 12 🔄 5 ❤️ 32 👀 2784 📊 18 ⚡