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University of Modena and Reggio Emilia
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Aligning the least aligned attention heads in MLLMs can yield the most significant performance gains, challenging conventional alignment strategies.
DLMs reveal surprising trade-offs between performance and computational efficiency that challenge conventional wisdom about language model design.
Ditching text-based conditioning for visual features in diffusion-based super-resolution unlocks significantly improved structural fidelity and texture realism.
Forget training wheels: RaTA-Tool lets MLLMs pick the right tool for the job in the wild, even if they've never seen it before.
