@dair_ai: Can an LLM agent actually build a model of an environment it cannot see? This work makes the question gradeable. An age…
Summary
A research paper proposes agentic automata learning to evaluate whether LLM agents can infer hidden world models through interaction, finding that performance drops sharply as task complexity increases and that reasoning models outperform non-reasoning ones but still struggle.
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Can an LLM agent actually build a model of an environment it cannot see?
This 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.
The honest result is that performance drops sharply as the automaton grows. Reasoning models do better than the rest, but everything degrades with size.
Why does it matter?
World-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.
Paper: https://arxiv.org/abs/2606.16576
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Can LLM Agents Infer World Models? Evidence from Agentic Automata Learning
Source: https://arxiv.org/abs/2606.16576 View PDF
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.
Submission history
From: Reef Menaged [view email] **[v1]**Mon, 15 Jun 2026 11:23:13 UTC (688 KB)
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