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LLMs are surprisingly bad at automating the creation of executable visual workflows from natural language, highlighting a significant gap in their ability to translate intent into reliable, deployable code.
LLM agents can learn more efficiently by leveraging a Skill Knowledge Base automatically constructed from prior experiences, enabling weaker agents to achieve stronger performance.
LLMs can slash token usage by 70% and boost reasoning accuracy by 14.8% in long-horizon tasks simply by learning when to remember and forget intermediate thoughts.
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.
LLMs can now evaluate research ideas like human experts, thanks to a new framework that grounds them in external knowledge and diverse perspectives.
