[2606.16576] Can LLM Agents Infer World Models? Evidence from Agentic Automata Learning
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Computer Science > Computation and Language
arXiv:2606.16576 (cs)
[Submitted on 15 Jun 2026]
Title:Can LLM Agents Infer World Models? Evidence from Agentic Automata Learning
Authors:Reef Menaged, Gili Lior, Shauli Ravfogel, Roee Aharoni, Gabriel Stanovsky<br>View a PDF of the paper titled Can LLM Agents Infer World Models? Evidence from Agentic Automata Learning, by Reef Menaged and 4 other authors
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Abstract:We propose agentic automata learning to evaluate the extent to which tool-calling LLM agents can uncover hidden environments through interaction. In our setup, an agent should uncover a hidden deterministic finite automaton (DFA) by interacting with an oracle through (1) membership queries ("Does this string belong to the target language?") and (2) equivalence queries ("Is this the target DFA?"). This yields a scalable testbed with controlled task complexity, measurable interaction efficiency, and strong baselines (classic automata-learning algorithms). Evaluating state-of-the-art LLMs, we find that performance drops sharply as DFA size increases. Reasoning models are markedly stronger than non-reasoning models, yet trajectory analyses reveal recurring failures in query planning, evidence integration, and hypothesis construction. Overall, our results show that current LLM agents can sometimes perform non-trivial interactive discovery, but remain far less robust and efficient than classic algorithms for the task.
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Computation and Language (cs.CL)
Cite as:<br>arXiv:2606.16576 [cs.CL]
(or<br>arXiv:2606.16576v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.16576
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arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Reef Menaged [view email]<br>[v1]<br>Mon, 15 Jun 2026 11:23:13 UTC (688 KB)
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