arXiv:2606.16576v1 Announce Type: new 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 algor
The paper demonstrates an actionable method for evaluating LLM agents' ability to autonomously learn about hidden environments, a critical step toward more capable and general AI agents.
This research provides a scalable and controlled testbed for understanding and improving the 'world model' capabilities of AI agents, which is foundational for their broader application and impact.
The ability to systematically test and quantify an LLM agent's capacity to infer hidden systems through interaction marks a significant advancement in AI agent development and evaluation.
- · AI Agent developers
- · Hyperscalers
- · Software automation sector
- · Legacy enterprise software
- · White-collar task-based roles
Improved LLM agents capable of learning complex system interactions more efficiently.
Acceleration in the deployment and impact of autonomous AI agents across various industries.
Potential for AI agents to independently discover and optimize processes in highly complex environments, leading to unforeseen efficiencies and disruptions.
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Read at arXiv cs.CL