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An end-to-end deep learning framework from Stanford University, named the Attention Factor Model, jointly optimizes factor identification, mispricing detection, and trading policy by explicitly incorporating transaction costs. The model demonstrates an annualized net Sharpe ratio of 2.28 in out-of-sample tests on U.S. equities, substantially outperforming prior two-step statistical arbitrage approaches.