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COMPANY · ENTITY #369

Low-Rank Adaptation (LoRA)

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RESEARCH · 1 SOURCE · arXiv cs.AI

LSA-PAST: parameter-efficient adapters with prototype transport for compositional incremental learning

The paper (arXiv:2610.27203v1) introduces LSA-PAST, a method that integrates LoRA-style, depth-specialized adapters into a frozen ViT for states, objects and compositions, and uses a rehearsal-free affine transform (PAST) to map past prototypes into the current adapter space so old prototypes remain compatible as new compositions arrive; experiments report state-of-the-art accuracy–forgetting trade-offs on compositional incremental learning benchmarks.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

FLoKD: Adaptive knowledge distillation for federated LoRA fine-tuning of LLMs over wireless networks

This arXiv preprint proposes FLoKD, an adaptive knowledge-distillation framework for federated LoRA fine-tuning of large language models over bandwidth‑constrained wireless networks. Instead of sharing full parameters or token logits, FLoKD transmits intermediate LoRA activations and selectively sends only important transformer blocks and public samples aligned with local distributions; experiments on WikiText-103, PTB and a dialog dataset report 50–65% communication reduction while converging to competitive perplexity compared to baselines.

6.0