arXiv AI August 18, 2026 By Zirui Song, Yuye Zhu, Yang Yang

Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps

arXiv:2608. 16309v1 Announce Type: cross Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language

Sep 1

Structural Anchor Pruning: Training-Free Multi-Vector Compression for Visual Document Retrieval

arXiv:2601.20107v3 Announce Type: replace-cross Abstract: Recent Vision-Language Models (e.g., ColPali) enable fine-grained Visual Document Retrieval (VDR) but incur prohibitive multi-vector index st...

By Zhuchenyang Liu, Ziyu Hu, Yao Zhang, Yu Xiao

llms fine-tuning efficiency multimodal safety

arXiv Computation and Language

Sep 10

Better Together: Complementary Query Rewriting Under a Strong RAG Baseline

arXiv:2609.05637v2 Announce Type: replace Abstract: A popular way to improve Retrieval-Augmented Generation (RAG) is to rewrite the user's question into several variants and search with all of them....

By Sara Shanian, Xiaoqin Yi, Pavlo Ruban, Kurt MacDonald

llms rag

arXiv AI

Aug 3

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation

arXiv:2603. 17205v3 Announce Type: replace-cross Abstract: Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process.

By Haoyang Fang, Shuai Zhang, Yifei Ma, Hengyi Wang, Cuixiong Hu, Katrin Kirchhoff, Bernie Wang, George Karypis

llms rag fine-tuning efficiency

arXiv AI

Aug 25

Semantic Compression Trees: Multi-Resolution Knowledge Retrieval via Hierarchical Semantic Residuals

arXiv:2608.21610v1 Announce Type: new Abstract: Retrieval-augmented generation relies mostly on flat, fixed-granularity indexes: documents are cut into uniform chunks and retrieved by similarity, dis...

By Junaid Farooq

llms rag benchmarks

arXiv Machine Learning

Jun 9

Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy

arXiv:2606. 09080v1 Announce Type: new Abstract: Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove computation across tokens, layers, heads, dimensions, and attention patterns.

By Haozhe Hu, Hao Wu, Anhao Zhao, Longwei Ding, Peiran Yin, Yunpu Ma, Xiaoyu Shen

llms nlp efficiency benchmarks

arXiv Machine Learning

Aug 26

RetrievalFormer: A Dual-Encoder Transformer for Efficient Approximate Nearest Neighbor Retrieval and Cold-Item Recommendation

arXiv:2608.24079v1 Announce Type: cross Abstract: A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering bo...

By Theodore Rogers, Joe Standerfer, Dmitrii Timoshenko, Haoxue Li, Zuhaib Akhtar, Soyoung Yang

llms