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The hardest AI tasks remain largely unsolved, with current models achieving only a 2.6% success rate on economically valuable workflows.
Explicitly training LLMs to verbalize confidence scores and signal reasoning-time uncertainty unlocks better calibration, failure detection, and control in retrieval-augmented generation.
Achieve safe and efficient real-world robot control by continually adapting policies trained in simulation, overcoming the limitations of fixed policies and wide randomization ranges.
