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Batch normalization's power comes from reshaping the geometry of neural network decision boundaries on a per-batch basis, not just from optimization benefits.
Chain-of-thought reasoning can actually *hurt* language agent performance in function-calling tasks, with brief reasoning outperforming both direct answers and lengthy deliberation.
