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Even state-of-the-art MLLMs fail to accurately reconstruct academic documents, revealing a critical gap in machine understanding of scientific knowledge.
MobileMem shifts the paradigm from static information retrieval to dynamic experiential learning, enabling AI agents to evolve alongside their users.
EvoThink reduces inference-time token usage by pruning redundant reasoning steps while boosting LRM performance through the optimization of valuable failed attempts.
LLMs struggle with code migration when APIs evolve, but KCoEvo's knowledge graph augmentation boosts migration accuracy and execution success.
Chain-of-Thought prompting doesn't always improve LLMs' ability to solve discrete optimization problems, and surprisingly, "disordered" datasets can sometimes boost performance on simpler tasks.
AI agents can now learn durable skills instead of constantly "reinventing the wheel," thanks to SkillNet's infrastructure for creating, evaluating, and connecting AI skills at scale.
