arXiv:2603.10652v3 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion. Under such conditions, their understanding and reasoning degrade substantially, revealing a gap between clean, controlled (i.e., unperturbed) evaluation settings and real-world robustness. To address this limitation, we propose ROVA, a novel training framework that improves robustness by modeling a robustness-aware consistency reward under spatio-temporal corruptions. ROVA introduces a difficulty-aware online trai
The proliferation of vision-language models in real-world applications highlights an urgent need to address their robustness issues outside of controlled environments, driving innovation in this area.
This development is crucial for expanding the practical utility and reliability of autonomous systems, robotics, and AI agents in dynamic, unpredictable settings.
AI models will become significantly more resilient to real-world disturbances, closing the gap between laboratory performance and field deployment for computer vision and reasoning tasks.
- · Robotics companies
- · Autonomous vehicle industry
- · AI agents developers
- · Surveillance technology providers
- · Companies relying on brittle or unrobust AI models
- · AI systems lacking adaptive robustness
- · Developers ignoring real-world data distribution shifts
Improved reliability and broader deployment of AI-powered systems in complex environments.
Accelerated development and adoption of AI in sectors requiring high resilience, like defense and infrastructure monitoring.
Enhanced trust in autonomous decision-making systems, potentially leading to more decentralized AI operations.
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Read at arXiv cs.AI