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arXiv AI By Yezhou Cheng, Runjia Du, Zeming Liu, Hang Lyu, Zehua Yang, Bojun Lin

Knowing When to Yield: Grounded Arbitration of User Corrections in Text-Based Embodied Agents

Read the original on arXiv AI →

The paper introduces GAVA, a grounded arbitration framework that enables text-based embodied agents to decide whether to accept, reject, inspect, or ask for clarification when a user’s correction might be incorrect. In the ALFWorld environment, GAVA achieves perfect correction accuracy through local inspections and reduces interaction costs compared to always-verify baselines, especially when leveraging semantic priors. The study demonstrates that selective information gathering can lower declared joint costs, though it does not conclusively prove a general advantage of environmental value of information over clarification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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