{"slug": "can-llm-agents-infer-world-models-evidence-from-agentic-automata-learning", "title": "Can LLM Agents Infer World Models? Evidence from Agentic Automata Learning", "summary": "A June 15, 2026 arXiv paper introduces agentic automata learning, a testbed in which tool-calling LLM agents must uncover a hidden deterministic finite automaton (DFA) through membership and equivalence queries to an oracle. Evaluating state-of-the-art LLMs, the authors find performance drops sharply as DFA size increases, with reasoning models markedly stronger than non-reasoning models but still far less robust and efficient than classic automata-learning algorithms. Trajectory analyses attribute recurring failures to query planning, evidence integration, and hypothesis construction.", "body_md": "# Computer Science > Computation and Language\n\n  [Submitted on 15 Jun 2026]\n\n# Title:Can LLM Agents Infer World Models? Evidence from Agentic Automata Learning\n\n[View PDF](/pdf/2606.16576)\n\n[HTML (experimental)](https://arxiv.org/html/2606.16576v1)\n\nAbstract: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.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/can-llm-agents-infer-world-models-evidence-from-agentic-automata-learning", "canonical_source": "https://arxiv.org/abs/2606.16576", "published_at": "2026-09-10 02:09:17+00:00", "updated_at": "2026-09-10 02:20:35.757225+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["arXiv", "LLM agents", "deterministic finite automaton", "agentic automata learning"], "alternates": {"html": "https://wpnews.pro/news/can-llm-agents-infer-world-models-evidence-from-agentic-automata-learning", "markdown": "https://wpnews.pro/news/can-llm-agents-infer-world-models-evidence-from-agentic-automata-learning.md", "text": "https://wpnews.pro/news/can-llm-agents-infer-world-models-evidence-from-agentic-automata-learning.txt", "jsonld": "https://wpnews.pro/news/can-llm-agents-infer-world-models-evidence-from-agentic-automata-learning.jsonld"}}