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The article describes an incident where an AI support agent autonomously issued refunds based on legitimate context but outside the intended scope, highlighting challenges in scoping tool permissions to specific intents in agentic AI systems.
IntentVLA is a history-conditioned visual-language-action framework that improves robot imitation learning stability by encoding short-horizon intents from visual observations, addressing challenges from partial observability and ambiguous observations. It also introduces AliasBench, an ambiguity-aware benchmark for evaluating such methods.