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Text world models can transform LLM-based agents from reactive responders to proactive planners, fundamentally changing how they interact with complex environments.
LLM agents can substantially improve their task-solving abilities by treating skills as long-lived, experience-aware, and testable assets within a managed lifecycle.
LLM agents can get a ~5% performance boost and 4x memory reduction by representing workflows as graphs and adaptively instantiating them based on task semantics.
