Semantic Flip: Synthetic OOD Generation for Robust Refusal in Embodied Question Answering and Spatial Localization
arXiv:2606.16898v1 Announce Type: cross Abstract: Detecting unanswerable user queries remains essential for the reliable deployment of real-world embodied agents. However, modern vision-language models (VLMs) often generate overly confident answers even when the available visual memory cannot support the query. Such overconfidence poses various task-dependent risks. The agent may provide misleading information to the user in Embodied Question Answering and select an arbitrary coordinate and physically guide the user there in spatial reasoning for navigation. Despite these high stakes, only a f
As AI models become more integrated into real-world applications, solving the 'hallucination' problem of overconfidence in unanswerable queries is critical for safe and reliable deployment.
Improving the robustness and refusal capabilities of embodied AI agents is essential for preventing misleading information, reducing risks in sensitive applications, and building user trust.
The development of synthetic out-of-distribution generation techniques directly addresses a key limitation in current vision-language models, enabling more reliable AI agent behavior in uncertain situations.
- · AI safety researchers
- · Embodied AI developers
- · Users of embodied AI systems
- · Developers of overconfident AI models
- · Early adopters of unreliable embodied AI
More reliable AI agents will be deployed in complex or safety-critical environments.
Increased user trust and wider adoption of embodied AI applications will follow from improved reliability.
The enhanced safety and predictability of AI agents could accelerate regulatory frameworks and public acceptance of autonomous systems.
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Read at arXiv cs.AI