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AURA is proposed, an ultra-efficient representation-editing method that freezes the pretrained model and applies sparse scale-and-shift edits to decoder cross-attention heads, supporting dynamic activation editing as a practical path for grounding AED speech models.
Encoder Awakening via Adapters (EAVA), a simple yet effective domain-adaptive fine-tuning method for Speech-LLM-based ASR, consistently outperforms vanilla fine-tuning and other baselines, achieving new state-of-the-art performance.