arXiv:2606.26904v1 Announce Type: cross Abstract: Video reasoning language models implicitly assume that every input frame is equally reliable. This leads to what we term the Blind Trust Problem: under realistic perturbations such as motion blur, glare, or occlusion, frontier video reasoning models can suffer 15-30%p accuracy drops on real-world embodied benchmarks, while remaining unaware that their visual evidence has been degraded. To address this challenge, we propose Robust-TO, an agentic video understanding framework that explicitly integrates per-frame trustworthiness into every stage o
As AI models become more pervasive in real-world applications, ensuring their reliability and robustness in imperfect conditions is a critical and emerging challenge.
This development allows AI systems to make more reliable decisions by explicitly accounting for uncertainties in their visual inputs, crucial for deployment in dynamic environments.
The paradigm shifts from implicitly trusting all AI inputs to actively assessing and integrating per-frame trustworthiness, leading to more robust and aware AI agents.
- · AI agents developers
- · Robotics industry
- · Computer vision researchers
- · Autonomous systems
- · Developers of 'blind trust' AI systems
AI video understanding systems will exhibit significant accuracy improvements under real-world perturbations.
This improved reliability will accelerate the adoption of AI agents in safety-critical applications.
Increased trust in AI perception could lead to broader integration of autonomous systems into daily life and industry.
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Read at arXiv cs.AI