PosIR employs length-controlled bucketing and span-based relevance to diagnose position bias across 10 languages, revealing that models favor early content, especially in long documents.

Ziyang Zeng, Dun Zhang, Yu Yan et al.

Proposed STELLA framework combines terminology-aware extraction and synthetic query generation to evaluate aerospace IR models, highlighting semantic understanding with specific results.

Bongmin Kim

ScienceDB AI uses LLM to recommend scientific datasets, achieving 30% accuracy improvement.

Qingqing Long, Haotian Chen, Chenyang Zhao et al.

OxygenREC employs a Fast-Slow thinking architecture integrating world knowledge, enhancing deep reasoning and multi-scenario recommendation efficiency.

Xuegang Hao, Ming Zhang, Alex Li et al.

Structured Spectral Reasoning (SSR) decomposes multimodal signals into spectral bands, employs band masking, hyperspectral fusion, and contrastive regularization to boost recommendation accuracy and robustness.

Wei Yang, Rui Zhong, Yiqun Chen et al.

Proposes SPLADE and Extended-SPLADE models with pruning strategies for billion-scale web document retrieval, balancing effectiveness and efficiency.

Taeryun Won, Tae Kwan Lee, Hiun Kim et al.

Proposes HHFT, a hierarchical heterogeneous feature Transformer, achieving +0.4% CTR AUC improvement and +0.6% GMV uplift on Taobao platform.

Liren Yu, Wenming Zhang, Silu Zhou et al.

This study systematically evaluates multilingual embedding models, contrastive learning, and re-ranking, showing dense retrieval surpasses translation-based methods with over 15% improvement in Recall@100.

Roksana Goworek, Olivia Macmillan-Scott, Eda B. Özyiğit

Introduces weighted Token importance in ColBERT, improving Recall@10 by 1.28% zero-shot and 3.66% with fine-tuning.

Archish S, Ankit Garg, Kirankumar Shiragur et al.

QueryGym is an open-source Python toolkit supporting multiple LLM query reformulation methods, integrating BEIR and MS MARCO benchmarks, significantly improving retrieval performance.

Amin Bigdeli, Radin Hamidi Rad, Mert Incesu et al.

DiffuGR uses diffusion language models to generate DocIDs, enhancing retrieval accuracy and efficiency.

Xinpeng Zhao, Zhaochun Ren, Yukun Zhao et al.

QueStER uses lightweight LLMs to generate keyword queries, combining traditional BM25 retrieval with learned query reformulation, achieving +4.0 nDCG@10 over BM25.

Arthur Satouf, Yuxuan Zong, Habiboulaye Amadou-Boubacar et al.

LiCoMemory uses hierarchical CogniGraph for lightweight, structured long-term memory, boosting reasoning efficiency by 23%.

Zhengjun Huang, Zhoujin Tian, Qintian Guo et al.

OneTrans employs a unified Transformer framework for joint feature interaction and sequence modeling, boosting industrial recommendation accuracy.

Zhaoqi Zhang, Haolei Pei, Jun Guo et al.

PROSPER framework leverages LLMs with residual networks and coarse-to-fine sparsification, achieving 10.2% recall gain in product search.

Hongru Song, Yu-an Liu, Ruqing Zhang et al.

Proposes LAC layout for generative recommendation, balancing signal maximization, causal fidelity, and efficiency, reducing FLOPs by 40%.

Xiaokai Wei, Jiajun Wu, Daiyao Yi et al.

Proposes Critic-LLM-RS combining collaborative filtering with pre-trained LLMs for improved recommendation without fine-tuning.

Zhisheng Yang, Xiaofei Xu, Ke Deng et al.

ReRe employs RLVR with constrained beam search and combined rewards to significantly boost recommendation ranking performance.

Junfei Tan, Yuxin Chen, An Zhang et al.

Proposed OneRec-Think integrates dialogue, reasoning, and personalized recommendation, using multimodal alignment and reinforcement learning to enhance interpretability and industrial deployment.

Zhanyu Liu, Shiyao Wang, Xingmei Wang et al.

HatLLM uses hierarchical attention masks to strengthen cross-item collaboration, achieving a 9.13% average gain.

Yu Cui, Feng Liu, Jiawei Chen et al.