arXiv:2605.08747v4 Announce Type: replace Abstract: Standard embodied evaluations do not independently score whether an agent correctly commits to task completion at episode closure, a capacity we call terminal commitment. Behaviorally distinct failures--never completing the task, completing it but failing to stop, and reporting success without sufficient evidence--collapse into the same benchmark failure. We introduce VIGIL, an evaluation framework that makes terminal commitment independently measurable. Under VIGIL's default protocol, agents observe only egocentric RGB, receive no action-suc
The increasing sophistication of embodied AI agents necessitates more granular evaluation metrics to identify subtle but critical failure modes beyond simple task completion.
This research addresses a fundamental limitation in current embodied AI evaluation, which conflates distinct types of agent failures and thereby hinders targeted improvement and development.
A new evaluation framework, VIGIL, introduces independent scoring for 'terminal commitment,' clarifying whether an agent truly understands task completion and proper termination.
- · AI researchers in embodied agents
- · Developers of general-purpose AI
- · Industries deploying autonomous robots
- · Legacy AI evaluation methodologies
Embodied AI agents will be trained and evaluated with a more precise understanding of their decision-making at task resolution.
This improved evaluation could accelerate the development of more reliable and robust autonomous systems by clarifying areas for improvement.
The concept of 'terminal commitment' might generalize to other AI domains, leading to more human-aligned self-termination processes across diverse AI applications.
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Read at arXiv cs.AI