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Self-evolving knowledge graphs can dramatically enhance multimodal reasoning by continuously adapting to new information and correcting errors in real-time.
A unified framework reveals that existing LLM policy optimization methods often overlook compound failures that require simultaneous adjustments to both trajectory and reward components.
LLMs are still far from being able to generate expert-level clinical guidelines, despite advances in deep research systems.
