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CASTER achieves superior performance in frozen model adaptation while requiring 18x less state, challenging the conventional reliance on gradient-based methods.
Spectral diagnostics reveal that hallucinations in LLMs can be detected with unprecedented accuracy by analyzing the thermodynamic properties of attention graphs.
AdapterTune's zero-initialization trick unlocks stable and efficient transfer learning for frozen Vision Transformers, achieving better accuracy than full fine-tuning with significantly fewer trainable parameters.