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This work presents OmniRisk, an omnidirectional planning framework that learns trajectory-level risk offline for efficient onboard evasion and enables efficient onboard avoidance, with real-world flights demonstrating consecutive evasive maneuvers at relative encounter speeds up to 15 m/s without fine-tuning.
Normalizing claims before verification can boost citation classification performance by over 10%, revealing the crucial role of evidence quality.
Achieving 93.02% accuracy in molecular structure recognition, MinerU.Chem outperforms existing systems, unlocking new possibilities for AI-driven chemistry research.
