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Existing benchmarks miss the mark on faithfulness, but a new dependency-aware checklist reveals the true performance gaps in T2I models.
Rethinking supervised fine-tuning as target distribution design reveals that optimizing token likelihood may overlook richer model knowledge, leading to significant performance gains.
