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  "text": "Cool paper from PwC.\n\n\"Earlier is always better\" is the default intuition for agent clarification. New paper claims that's mostly wrong.\n\nGoal clarification loses nearly all of its value after just 10% of execution.\n\nThe team built a forced-injection framework that drops ground-truth clarifications at controlled points along a long-horizon agent's trajectory, across 4 information dimensions (goal, input, constraint, context), 3 benchmarks, and 4 frontier models. 84 task variants, 6,000+ runs.\n\nPass@3 falls from 0.78 back to baseline. Input clarification keeps value through roughly 50%. Past mid-trajectory, asking any clarification at all performs worse than never asking.\n\nA complementary study of 300 unscripted sessions shows no current frontier model asks within the empirically optimal window. 52% of sessions over-ask. Others never ask at all.\n\nWhy it matters: clarification has been treated as a binary capability, does the agent ask or not. This is the first quantitative demand curve for *when* the question is worth asking.\n\nPaper: https://t.co/U4prpHjKgP\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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