Can Agents Infer Environment from Interaction? Evidence from Agentic Automata Learning
We propose agentic automata learning to evaluate the extent to which tool-calling LLM agents can uncover hidden environments through interaction, a capability increasingly required in agentic tasks (e.g., reproducing an executable without access to its source code by interacting with it). 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?''). Agentic automata learning yields a scalable testbed with controlled task complexity, measurable interaction efficiency, and strong algorithms to compar...
| Authors | Reef Menaged, Gili Lior, Shauli Ravfogel et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 27 September 2026 |
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