Tool-calling agents are weak at inferring world models from interaction.

A paper from Stanovsky's group (arXiv:2606.16576) tests whether LLM agents can uncover a hidden deterministic finite automaton through membership and equivalence queries. Performance drops sharply as the automaton grows, and trajectory analysis exposes recurring failures in query planning, evidence integration, and hypothesis construction. Reasoning models beat non-reasoning ones, but not enough. Concrete evidence that current agents are bad at active environment exploration, which matters a lot if you're designing agents expected to discover system behavior on their own. They're better at executing known plans than at figuring out how an unknown system works.