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"
