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A unified framework reveals that most optimizers only engage a fraction of their potential, providing a roadmap for more effective model training.
Real-world GUI agents can achieve over 72% success in complex mobile tasks by leveraging a unique hybrid training approach that integrates real-device execution and adaptive learning from failures.
SPOT-E transforms frozen VLMs into more reliable evidence readers by dynamically spotlighting critical visual information during inference.
A principled framework for General World Models reveals the limitations of current systems and the architectural requirements for future progress.
