arXiv Artificial Intelligence

Locality-Aware Redundancy Pruning for LLM Depth Compression

10 days ago 01.09.2026 07:00 👁 1 views
Locality-Aware Redundancy Pruning for LLM Depth Compression

Quick summary

arXiv:2605.27786v3 Announce Type: replace-cross 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. We show that inter-layer redundancy can be either localized or globally distributed depending on the LLM architect

Key takeaways

  • arXiv:2605.27786v3 Announce Type: replace-cross 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.

Why it matters

The importance of “Locality-Aware Redundancy Pruning for LLM Depth Compression” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗