LoRA-RC: Reservoir Computing with Low-Rank Adaptation

arXiv:2608.28295v1 Announce Type: new Abstract: Reservoir Computing (RC) designs Recurrent Neural Networks around a fixed, i.e., untrained, recurrent layer, and is a natural

arXiv:2604.27796v1 Announce Type: new Abstract: Exponential growth in the scale of modern foundation models has led to the widespread adoption of Low-Rank Adaptation (LoRA) as a

arXiv:2609.32002v1 Announce Type: new Abstract: The rank $r$ in LoRA is widely treated as a capacity control: a smaller rank is assumed to yield a simpler model that generalizes

arXiv:2606.12883v1 Announce Type: new Abstract: In Low-Rank Adaptation (LoRA), the scaling factor $\alpha$ is often treated as a mere complement to the learning rate, yet its role

arXiv:2602.22911v4 Announce Type: replace-cross Abstract: Low-Rank Adaptation (LoRA) dominates parameter-efficient fine-tuning (PEFT). However, it faces a critical ``linear

arXiv:2605.04115v1 Announce Type: cross Abstract: Learning in neural systems arises from synaptic changes that reshape the representations underlying behavior. While low-rank