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This work proposes UniRec, a Unified Cross-stage Recommendation Fusion model, and introduces a dual-axis preference alignment objective, and finds that unconstrained end-to-end fusion optimization can exploit imbalances in item attribute distributions, over-concentrating on high-reward regions at the cost of other objectives.
