ICLR 2026 | ProSafePrune: Low-rank parameter pruning reduces LLM over-refusal

ICLR 2026 | ProSafePrune：一剪见效，告别大模型过度防御

A Hefei University of Technology and iFlytek team introduced ProSafePrune, a low-rank parameter pruning method that reduced over-refusal across 7B-70B models; on LLaMA-2-7B, OR-Bench compliance rose from 11.0% to 73.0%. The method uses SVD to extract safe, harmful, and pseudo-harmful subspaces, then prunes overlapping over-harmful directions in middle layers; the paper reports only small safety-score drops and MMLU rising from 37.1 to 39.6. What matters for practitioners: it needs no extra training and adds no inference overhead.

Why it matters: HKR-H/K/R all pass: using pruning to reduce over-refusal is a novel hook, and the post includes 7B-70B scope, OR-Bench 11.0→73.0, MMLU 37.1→39.6, plus no extra training or inference cost. Featured, not p1, because this is still a research result, not a major product or industry-m