RankUp enhances representation rank via randomized permutation, multi-embedding, and global integration, boosting large-scale recommendation performance.

Jin Chen, Shangyu Zhang, Bin Hu et al.

CoARS framework enhances recommender systems using self-distilled reinforcement learning, showing improved user alignment in experiments.

Zongwei Wang, Min Gao, Hongzhi Yin et al.

RAR combines retrieval augmentation and reinforcement learning to improve recommendation performance and factuality, outperforming baselines on movie recommendation benchmarks.

Zhenrui Yue, Honglei Zhuang, Zhen Qin et al.

SMTPO integrates multi-task fine-tuning and reinforcement learning to optimize multi-turn user preferences, significantly boosting LLM-based conversational recommendation accuracy.

Xingyuan Xiang, Xiangchen Pan, Wei Wei

Multi-agent video recommenders with LLMs enhance precision and explainability.

Srivaths Ranganathan, Abhishek Dharmaratnakar, Anushree Sinha et al.

UniMixer introduces a unified recommendation scaling architecture with parameterized TokenMixer, combining attention, TokenMixer, and FM advantages for improved efficiency.

Mingming Ha, Guanchen Wang, Linxun Chen et al.

Proposes AgenticRS, a self-evolving recommendation framework using reinforcement learning and LLMs for autonomous module optimization.

Jinxin Hu, Hao Deng, Lingyu Mu et al.

AutoModel employs three core agents—AutoTrain, AutoFeature, AutoPerf—for full lifecycle automation, reducing manual effort and improving recommendation performance.

Hao Zhang, Jinxin Hu, Hao Deng et al.

UniScale combines entire-space data expansion and hierarchical Transformer architecture to boost search ranking performance.

Liren Yu, Caiyuan Li, Feiyi Dong et al.

Proposes SIDReasoner, enhancing SID–language alignment and using outcome-driven reinforcement to improve reasoning in generative recommendation, boosting accuracy and interpretability.

Yingzhi He, Yan Sun, Junfei Tan 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.

OneRec-V2 achieves 49% latency reduction and 92% throughput increase via FP8 quantized inference.

Yi Su, Xinchen Luo, Hongtao Cheng et al.

Study shows LLM-generated pseudo-relevance feedback significantly improves query performance, especially in low-resource tasks.

Nour Jedidi, Jimmy Lin