Video AI has been reasoning over blurred and occluded footage while trusting every frame equally. This work tackles that blind spot. Title: Confidence-Aware Tool Orchestration for Robust Video Understanding URL: ❓ What's the problem? 💡 Video-LLMs implicitly assume every frame is equally reliable (the authors' "Blind Trust Problem"). When footage degrades from motion blur, glare, or occlusion, they fail to notice and lose 15-30 points on real-world benchmarks, while their self-reported confidence barely changes, a silent failure. ❓ How does Robust-TO solve it? 💡 It bakes per-frame trustworthiness into every reasoning stage. First, quality profiling scores blur, brightness, and occlusion to keep only reliable frames; then it decomposes the query into sub-queries routed to tools robust to the dominant corruption, and every tool returns a (result, confidence) pair. ❓ How is confidence used? 💡 Evidence is grouped into high/medium/low tiers. High drives the conclusion, medium is kept only when consistent, low is a fallback only, and any residual uncertainty is stated in the answer. It is trained with GRPO using a confidence-cost reward. ❓ How well does it work? 💡 56.4% average on clean video (+10.6pt over Gemini-2.5-Pro) and 54.3% under corruption (+5.8pt over the strongest open-source Video-R1). It also trims frames from 32 to 20.7, cutting inference time by over 35% while gaining +1.6pt accuracy. #VideoUnderstanding# #MultimodalAI#