这篇论文重新定义了AGI的评判标准——从“回答能力”转向“探索能力”，做智能体研究的团队值得仔细读，它可能改变你对AI发展路径的理解。
一篇来自中美顶级实验室的111页综述论文提出，AGI的关键不在于模型回答得更好，而在于智能体能否主动探索未知。论文定义了“认知探索”概念，即智能体应主动减少不确定性，在能力边界附近学习，并保持未来路径的开放性。探索不是随机行为，而是有纪律地询问哪些观察会改变信念、哪些尝试能提升技能。论文将AI进展分为5个层级：响应者、推理者、智能体、探索者和生态系统，每个层级探索更广阔的空间。
AGI needs agents that actively explore what they d…
AGI needs agents that actively explore what they do not know, not just models that answer better.
This new large (111 page) survey paper from from top labs across US and China talks about epistemic exploration, which means an agent should actively reduce uncertainty, learn near the edge of what it can do, and keep future paths open.
Exploration is not randomness; it is the disciplined act of asking which observation would change your beliefs, which attempt would improve your skill, and which path must remain open before it closes.
It breaks this into 3 needs: seek useful information, turn hard-but-learnable experiences into better ability, and avoid getting stuck in one narrow strategy too early.
The authors organize AI progress into 5 levels: responder, reasoner, agent, prospector, and ecosystem, where each level explores a wider space than the last.
A responder mostly gives an answer, a reasoner searches through possible thoughts, an agent tests the outside world, a prospector simulates futures, and an ecosystem uses many agents working together.
Paper - "Agent Exploration Toward Artificial General Intelligence"