Using structured linked data as a memory layer improves RAG system retrieval accuracy by 29.6% in standard RAG and 29.8% in agentic pipeline.

Andrea Volpini, Elie Raad, Beatrice Gamba et al.

SORT integrates local attention, request-centric sampling, and generative pre-training, achieving 6.35% order increase and halving latency in industrial recommenders.

Chunqi Wang, Bingchao Wu, Taotian Pang et al.

STATIC vectorizes Trie decoding on TPU/GPU, reaching 0.033 ms per step with strict constraints.

Zhengyang Su, Isay Katsman, Yueqi Wang et al.

Introduces MIGRASCOPE, an MI-based framework to evaluate and optimize RAG retrievers, revealing redundancy and synergy with specific metrics.

Wenqing Zheng, Dmitri Kalaev, Noah Fatsi et al.

Proposes a feedback-loop model revealing that increasing recommendation adoption initially boosts but ultimately reduces individual diversity over time.

Gabriele Barlacchi, Margherita Lalli, Emanuele Ferragina et al.

Self-EvolveRec employs LLM-guided directional feedback with user simulators and model diagnosis tools to autonomously evolve recommender systems, outperforming NAS and code evolution baselines.

Sein Kim, Sangwu Park, Hongseok Kang et al.

DiffuRank leverages diffusion language models for document reranking, enabling parallel decoding and outperforming comparable autoregressive models in efficiency and effectiveness.

Qi Liu, Kun Ai, Jiaxin Mao et al.

LASER combines SeqVault with STA/GSTA for low-latency ultra-long CTR modeling, delivering +2.36% ADVV and +2.08% revenue online.

Tianhe Lin, Ziwei Xiong, Baoyuan Ou et al.

EST architecture with LCA and CSA achieves fully unified sequence modeling, significantly improving industrial CTR prediction performance.

Mingyang Liu, Yong Bai, Zhangming Chan et al.

OneLive introduces a dynamically unified generative recommendation framework with real-time content encoding and time-aware attention, significantly improving live-streaming performance.

Shen Wang, Yusheng Huang, Ruochen Yang et al.

TokenMixer-Large introduces mixing-reverting and sparse MoE, scaling models to 15B parameters, achieving significant offline and online performance gains.

Yuchen Jiang, Jie Zhu, Xintian Han et al.

Proposes MFTR, a multi-field tool retrieval framework using structured representations and dynamic weighting.

Yichen Tang, Weihang Su, Yiqun Liu et al.

RecGOAT integrates GNN and OT for dual-level semantic alignment, achieving state-of-the-art multimodal recommendation performance.

Yuecheng Li, Hengwei Ju, Zeyu Song et al.

MDGR uses masked diffusion for SID generation, improving over SOTA by up to 10.78% and increasing online revenue by 1.20%.

Lingyu Mu, Hao Deng, Haibo Xing et al.

Proposes a three-dimensional query performance prediction (QPP) evaluation framework (SRMQ-PP, MRSQ-PP, MRMQ-PP), demonstrating significant performance differences across models and tasks, with traditional score-based models excelling in multi-model scenarios.

Payel Santra, Partha Basuchowdhuri, Debasis Ganguly

Introduces Reddit-Amazon-EM dataset; evaluates SOTA entity matching methods; GNEM achieves 96.29% F1.

Zihan Huang, Rohan Surana, Zhouhang Xie et al.

Proposes a Nash Social Welfare-based fair recommendation algorithm balancing match rate and fairness.

Yoji Tomita, Tomohiko Yokoyama

This study demonstrates that leaking evaluation prompts and gold nuggets can inflate RAG system scores, highlighting vulnerabilities in LLM-based evaluation methods.

Laura Dietz, Bryan Li, Eugene Yang et al.

HyFormer unifies sequence modeling and feature interaction via global tokens, boosting CTR prediction by 3-5% over baselines.

Yunwen Huang, Shiyong Hong, Xijun Xiao et al.

Introducing 'Information Farming', a paradigm shift from passive harvesting to active cultivation using generative AI, with a farming analogy and empirical validation.

Leif Azzopardi, Adam Roegiest