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  "text": "Can an LLM agent actually build a model of an environment it cannot see?\n\nThis work makes the question gradeable. An agent has to uncover a hidden deterministic finite automaton by interacting with an oracle through membership queries (does this string belong?) and equivalence queries (is this the target?), with classic automata-learning algorithms as strong baselines.\n\nThe honest result is that performance drops sharply as the automaton grows. Reasoning models do better than the rest, but everything degrades with size.\n\nWhy does it matter?\n\nWorld-model claims about agents are usually vibes. Forcing an agent to actively reconstruct a hidden structure through queries is a clean, controlled way to measure whether it is modeling its environment or just reacting.\n\nPaper: https://t.co/Kw1WCLEAQ3\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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