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

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.