GAVA: Grounded arbitration of user corrections in text-based embodied agents (arXiv:2610.00282v1)
The paper formulates 'grounded correction arbitration' — a decision framework for agents to accept, reject, inspect, or ask about potentially incorrect user corrections — and introduces GAVA, which uses observation-bounded evidence, legal probes, and a one-step expected-loss rule. Evaluated in a text-only ALFWorld benchmark across many checkpoints and scenarios, GAVA achieves near-perfect correction accuracy while lowering interaction cost via selective inspection informed by an object-location prior, but the study is limited to symbolic observations, controlled speakers, and no human, visual, or robotic evaluation.