Aligning Dense Retrievers with LLM Utility via Distillation, UAE improves Recall@1 by 30.59% on QASPER benchmark.

Rajinder Sandhu, Di Mu, Cheng Chang et al.

Research evaluates QPP for selecting the best query variant in RAG pipelines to enhance generation quality.

Negar Arabzadeh, Andrew Drozdov, Michael Bendersky et al.

Introduced TAWin method using WPAUC to optimize RL-based recommenders, enhancing Top-K performance.

Wentao Shi, Qifan Wang, Chen Chen et al.

Proposes a complementarity fusion method for semantic and collaborative views, avoiding global alignment limitations to enhance recommender systems.

Maolin Wang, Dongze Wu, Jianing Zhou et al.

ResRank enhances retrieval efficiency and effectiveness via residual passage compression and end-to-end joint training.

Xiaojie Ke, Shuai Zhang, Liansheng Sun et al.

ECLASS-augmented dense retrieval method achieves 94.3% HitRate@5 in semantic search for electronic components.

Nico Baumgart, Markus Lange-Hegermann, Jan Henze

Diagnosable ColBERT enhances ColBERT model diagnostics by aligning token embeddings to a clinically-grounded reference latent space.

François Remy

LoopCTR enhances CTR prediction through loop scaling, significantly reducing computational costs.

Jiakai Tang, Runfeng Zhang, Weiqiu Wang et al.

CAST framework models semantic-level transitions, achieving 17.6% Recall and 16.0% NDCG gains with 65x training acceleration.

Qian Zhang, Lech Szymanski, Haibo Zhang et al.

CS3 framework enhances two-tower recommendation systems with Cycle-Adaptive Structure, Cross-Tower Synchronization, and Cascade-Model Sharing, achieving an 8.36% revenue increase.

Lixiang Wang, Shaoyun Shi, Peng Wang et al.

Document-as-image representations underperform in scientific retrieval; interleaved text+image representations are more effective.

Ghazal Khalighinejad, Raghuveer Thirukovalluru, Alexander H. Oh et al.

MARC method improves recommendation efficiency by modular representation compression, achieving a 2.82% eCPM lift in online tests.

Yunjia Xi, Menghui Zhu, Jianghao Lin et al.

Using BERT Score and expert evaluation, this study analyzes ChatGPT-4o, GeminiAI, and Perplexity AI's performance in generating Telugu maternal health responses, with Gemini leading.

Anagani Bhanusree, Sai Divya Vissamsetty, K VenkataKrishna Rao et al.

NanoVDR distills a 2B vision-language retriever into a 70M text-only encoder for visual document retrieval, retaining 95.1% of teacher quality.

Zhuchenyang Liu, Yao Zhang, Yu Xiao

Proposed EISAM framework significantly boosts tail-item recommendation performance.

Jiaming Zhang, Yuyuan Li, Xiaohua Feng et al.

AnchorRec prevents positional collapse in multimodal recommender systems using anchored alignment, enhancing recommendation accuracy.

Yonghun Jeong, David Yoon Suk Kang, Yeon-Chang Lee

FGTR achieves fine-grained multi-table retrieval via hierarchical LLM reasoning, improving F_2 by 18% on Spider and 21% on BIRD datasets.

Chaojie Sun, Bin Cao, Tiantian Li et al.

VLM4Rec enhances multimodal recommendation by leveraging large vision-language models for semantic representation.

Ty Valencia, Burak Barlas, Varun Singhal et al.

InterDeepResearch enables human-agent collaborative information seeking through an interactive deep research framework, enhancing process observability and real-time steerability.

Bo Pan, Lunke Pan, Yitao Zhou et al.

FedShare framework enhances recommendation performance through personalized data sharing and unlearning.

Liang Qu, Jianxin Li, Wei Yuan et al.