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Humanoid robots struggle to bridge the gap between selecting the right tool and executing complex tasks, with current policies showing significant performance degradation on unseen tools and unrelated instructions.
SafeBranch enables embodied agents to achieve ten times more safe task completions without sacrificing performance, even in novel environments.
Leading LLMs falter in Korean web-browsing tasks, achieving less than half the accuracy found in previous benchmarks.
Current function-calling benchmarks are too simple: DICE-BENCH reveals that LLMs still fail at realistic, multi-turn tool use.
