Track manipulation, locomotion and embodied systems.
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Even the best LLM agents fail nearly half of long-horizon coding tasks due to harness engineering flaws, with failures concentrating on how cross-cutting functionality is modularized.
LLM agents can dynamically evolve their skill libraries, preventing obsolescence and improving long-horizon task performance by up to 7.8% through a fitness-driven lifecycle.
A structured evidence-state approach can boost omni-modal QA accuracy by over 30%, transforming how agents gather and validate information across diverse sources.