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OneDayAgent achieves a groundbreaking 0.821 score on long-horizon tasks, proving that a single harness can effectively manage execution across diverse LLM backends without tuning.
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.
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.
