对比三种检索引擎，发现索引侧剪枝可跨平台通用，查询侧剪枝已过时。
Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps
- 在三种引擎上测试静态剪枝，发现索引侧剪枝效果稳定
- 索引大小减少18%-82%，延迟降低1.2-6.6倍，性能不降
- 适合追求高效检索的工程落地者，尤其关注内存与速度平衡
静态剪枝广泛用于加速稀疏神经检索，但现有研究多基于单一定制化流程，难以判断结论是否适用于现代引擎。本文首次开展跨引擎剪枝可迁移性研究，评估三种引擎（控制型C++流水线、BMP、SEISMIC）在两个基准（MS MARCO、Natural Questions）上的表现，使用两种编码器（SPLADE：44个平均查询词；V3-GTE：7个），共1140种配置，并在TREC DL 2019/2020上进行深度验证。结果表明，索引侧剪枝（文档与倒排列表）具有可迁移性：在所有引擎中均实现1.2-6.6倍延迟降低和18%-82%索引压缩，因稀疏检索受内存瓶颈制约，此结论经缓存未命中、TLB、IPC分析支持。相反，查询剪枝已被现代引擎内化：在完整索引上可提速4-11倍，但在BMP的β机制和SEISMIC的query_cut下被取代。静态剪枝可与动态剪枝互补：在BMP上结合文档与查询压缩，实现2.5倍提速，且NDCG@10仅下降0.003。同时，所有引擎中NDCG@10趋于饱和，而Recall@10保持在85%-95%区间，提供统一停止准则——可安全推进剪枝至该拐点而不影响排序质量。研究明确回答：什么可迁移（索引侧）、什么失效（查询侧）、什么仍有效（静态+动态协同）。
原文摘要 · Abstract (English)
Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with different index organizations and dynamic pruning mechanisms. We present the first cross-engine pruning portability study, evaluating static pruning strategies across three engines - a controlled C++ pipeline (exhaustive inverted index), BMP (block-max pruning), and SEISMIC (clustered inverted indexes) - on two benchmarks (MS MARCO, Natural Questions) with two encoders spanning opposite query-density regimes (SPLADE: 44 avg. query terms; V3-GTE: 7 avg. query terms), totaling 1,140 experimental configurations, with an additional deep-judgment validation on TREC DL 2019/2020. We find that index-side pruning (document and posting-list) is portable: it consistently reduces latency (1.2-6.6$\times$) and index size (18-82%) across all engines because sparse retrieval is memory-bound - a conclusion we support with cache-miss, TLB, and IPC profiling. In contrast, query pruning is already internalized by modern engines: it yields 4-11$\times$ speedup on the exhaustive pipeline but is subsumed by BMP's $β$ and SEISMIC's query_cut. Static pruning complements dynamic pruning: on BMP, combining document and query reduction yields 2.5$\times$ speedup with NDCG@10 within 0.003 of the exact baseline. Finally, NDCG@10 saturates while Recall@10 is still in the ${\sim}$85-95% range across all three engines, providing a portable stopping criterion: practitioners can push pruning to this knee without visible ranking degradation. Together, these findings answer what transfers (index-side pruning), what breaks (query pruning), and what still helps (static atop dynamic pruning).
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