AI RESEARCH
Locality-Aware Redundancy Pruning for LLM Depth Compression
arXiv CS.AI
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ArXi:2605.27786v1 Announce Type: cross 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