Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models

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arXiv:2610.07848v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing PEFT methods rely on uniform and static adaptations, without accounting for the structured heterogeneity of attention across dimensions, heads, layers, and input tokens. In practice, attention representations exhibit non-uniform behavior, and positional encoding mechanisms such as rotary positional emb