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When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning

arXiv cs.AI ↗ · 2026-08-05 缓存

This paper analyzes why compression statistics for LLM pruning can be reproducible yet select suboptimal endpoints, introducing information boundaries and observation fibers to model the gap. It proposes group-resolved and model-specific mask selection methods that improve worst-group perplexity across dense LLMs and OLMoE.

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