Adriano Mancini

Learnable filter shapes in RF-KAN yield a performance boost that outstrips conventional convolutional approaches while slashing parameter counts by over 80%.

Smaller, instruction-tuned LLMs can actually beat their larger counterparts in complex role-playing scenarios, thanks to the surprising discovery that bigger models often suffer from user-alignment bias and reduced consistency.