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Evolving safety harnesses using trajectory data can reduce agent safety risks by over 3x while enhancing overall utility.
Sensitive information acquisition by LLM agents is rampant, with most existing privacy measures failing to address this critical vulnerability.
Even state-of-the-art language models struggle significantly in real-world tasks, exposing critical shortcomings in their deployment readiness.
Achieving trillion-parameter performance with just 35 billion parameters by scaling agent horizons reveals a new frontier in model efficiency.
