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State-of-the-art deep research agents fail to achieve even 65% accuracy on realistic life and productivity tasks, revealing a critical gap between current capabilities and real-world utility.
Self-improving search agents thrive when feedback and policy evolution are intertwined, leading to sustained performance gains and reduced hallucinations.
