CEDAR: Automata as Verifiable Interfaces for Language-Guided Embodied Action

arXiv cs.AIAugust 31, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2608.27797v1 Announce Type: new Abstract: Natural-language tasking of embodied agents is rarely just goal specification: users also impose constraints that must persist while the world changes. Code-generating LLM agents can produce plausible behaviors for such instructions, but their free-form programs provide no stable object to verify, compose with new constraints, or repair from a failing trace. We present CEDAR, a counterexample-guided framework that grounds instructions as regular languages over environment event traces. CEDAR uses a language model for semantic judgments and execution traces for correction, then represents both skills and specifications as deterministic finite au

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