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It is found that generated harnesses remain substantially behind mature human-engineered references on code and on search and research, while matching or exceeding the selected references on writing and machine-learning experimentation, with large variation in execution cost.
High cognitive load elevated both subjective trust and behavioral reliance on AI, and cognitive constraints rebalance dual trust pathways-weakening analytic evaluation while amplifying heuristic outcome reliance-by revealing how cognitive constraints rebalance dual trust pathways.
This work introduces ASPIRE, a benchmark for vague-goal-driven self-evolution and shows that vague goals redirect search effort toward goal interpretation, and evaluates the resulting systems on a hidden, expert-authored set of 520 items spanning six goals.
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