Proposed Exp-RSFT method uses exponential reward weighting to enhance recommender systems' ranking performance.

Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu et al.

CCFormer integrates cross-field attention with hierarchical sequence compression, boosting recommendation accuracy by 3.57% CTR and 1.71% ad revenue, with 2.21× faster training.

Yunlong Wang, Huizhe Zhang, Haonan Hu et al.

VIG-RL employs reinforcement learning to dynamically search, select, and insert verified images, achieving state-of-the-art multimodal grounding.

Qinhan Yu, Jun Guang, Chong Chen et al.

DASH integrates cross-domain histories and thinking traces, enhancing decision-aware user simulation for online advertising.

Zipeng Chen, Jiaer Zheng, Xiangyang Xu et al.

SpecFormer introduces spectral softening and spectral-aware attention to mitigate embedding and attention collapse in recommendation Transformers.

Yu Cui, Yi Xu, Jiahao Wang et al.

T3EM achieves top retrieval performance (nDCG@10=0.638) but incurs 7-14x latency of open-source models.

Madhav S Baidya

SearchArt employs verification-driven synthetic data generation and multi-stage fine-tuning, achieving 74.39 on BrowseComp-ZH with only 27B parameters.

Lang Mei, Xiaohan Yu, Chong Chen et al.

DLMRec enhances recommender systems using discrete diffusion language models, significantly improving Recall and NDCG.

Chengyi Liu, Yongqi Zhou, Junwei Pan et al.

RAMP enhances ad recommendation accuracy via masking and alignment pathways, especially when personalized features are unavailable.

Dairui Liu, Zhongyi Lu, Roger Zhe Li et al.

Proposed Matryoshka Hypencoder with multi-scale Q-Net, reducing parameters by 7× while maintaining performance.

Majd Alkawaas, Sean MacAvaney

WHALE model integrates Wukong and HSTU architectures to enhance performance in large-scale recommendations.

Renqin Cai, Dawei Sun, Yuanjun Yao et al.

Proposes MESH, a modular retrieval framework that mitigates heterogeneity scaling bias, boosting sparse content scaling by 14× in Pinterest experiments.

Jiaxing Qu, Yilin Chen, Junpeng Hou et al.

SlimPer reformulates personalized ranking as iterative knowledge base refinement, decoupling depth from input length, improving efficiency and interpretability.

Siqi Wang, Xianjie Chen, Shaofeng Deng et al.

This study evaluates multilingual keyword and content variant injection attacks on LLM relevance judgments, revealing persistent inflation and evasion of defenses.

Nguyen Khoi Vo, Duy Duong Tuong, Oleg Zendel et al.

Behavior-grounded LLM judge using QRI cards improves relevance alignment by 5% overall and 91% in disagreement cases.

Ali Vardasbi, Gustavo Penha, Enrico Palumbo et al.

PlanRAG uses logical query trees to solve exploratory reasoning problems, improving accuracy.

Ganlin Xu, Linghao Zhang, Zhitao Yin et al.

SABER-Math constructs an automated, fine-grained benchmark for mathematical IR using large models, ontology, and preference ranking, outperforming traditional methods.

Nikolay Georgiev, Maria Drencheva, Kseniia Ibragimova et al.

KG4VD leverages multimodal knowledge graphs and graph retrieval, achieving 44.52% accuracy on long-range document VQA benchmarks.

Yi-Cheng Wang, Chu-Song Chen

NEXT framework uses NEXT-8B to improve video recommendations, achieving +0.53% watch time and +0.51% diversity.

Yuming Liu, Hongye Yang, Harrison Zhao et al.

PETRA leverages energy-domain classification and synthetic supervision to improve petroleum engineering retrieval, boosting first-stage nDCG from 0.703 to 0.763.

Kirill Dubovikov, Omar El Mansouri, Hachem Madmoun et al.