Locality-Aware Redundancy
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Locality-Aware Redundancy Pruning for LLM Depth Compression
arXiv:2605.27786v2 Announce Type: replace Abstract: Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing one-shot pruning methods rely on local layer importance or fixed redundancy assumptions across architectures. We propose Locality-Aware Redundancy Pruning (LoRP), a training-free one-shot depth pruning framework guided by representation locality.