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A simple, training-free framework in which a reasoning-capable vision-language model iteratively searches and reasons over Wikipedia, gathering evidence dynamically, shows that reasoning and retrieval are complementary on rare entities.
Personalized AI agents can achieve up to 20.9% better task success by learning from user feedback in real-time, reshaping the landscape of human-AI collaboration.
Reasoning-oriented training amplifies self-correction and uncertainty acknowledgment, yet fails to enhance the most predictive behaviors like confidence calibration, revealing a critical gap in model training.
Forget fine-tuning: Prompting MLLMs with a dynamic interval-based decoding strategy lets them generate surprisingly human-like, pause-aware real-time game commentary.
