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OCP: Outlier-Centric Probing for Dynamic Structured Pruning of LLMs (2026.acl-long)

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Challenge: Existing structured pruning methods fail to identify outlier-triggering tokens and uniform layer-wise sparsity misaligns with heterogeneous outlier distributions.
Approach: They propose a framework that prioritizes capturing outlier-triggering tokens rather than reconstructing full hidden distributions.
Outcome: Experiments on LLaMA2, LLama3 and OPT show that the proposed framework outperforms state-of-the-art methods and achieves 25% perplexity reduction at 1.6 speedup.

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