Information boundaries for group-robust LLM pruning; a reproducible compression statistic can select the wrong candidate, with a conic law modeling the pooling price for positive linear fixed-candidate damage.
Read the original at arxiv.org→arXiv:2608.02940v1 Announce Type: new Abstract: A reproducible compression statistic can still select the wrong candidate. A dense pruning score with 0.906 split-half reliability predicted a 16.1% gain. Its selected...
Original headline: "When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning"
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- Aug 5, 04:00 UTC arXiv cs.AI lead source When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning