CanonFedRec is proposed, a novel framework that achieves superior performance while reducing memory costs by up to 40× compared to the best FedRec approach and treats item-wise variance as a proxy for client-side cognitive divergence, and dynamically adapting optimization objectives via elastic decision boundaries.
Yun-Qi Mi, Ze-Yu Hao, Guo-Shuai Zhao et al.· Proceedings of the 32nd ACM...· 2 citations
This workshop aims to bring together researchers and practitioners to discuss recent advances, practical applications, and future directions of AI agents in information retrieval, while encouraging collaboration and knowledge exchange within the community.
Qing-Song Wen, P. Mehrotra, Yongfeng Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
CanonFedRec is proposed, a novel framework that achieves superior performance while reducing memory costs by up to 40× compared to the best FedRec approach and treats item-wise variance as a proxy for client-side cognitive divergence, and dynamically adapting optimization objectives via elastic decision boundaries.
Yunqi Mi, Zeyu Hao, Guoshuai Zhao et al.· Proceedings of the 32nd ACM...· 1 citation
Dynamic Multi-Path Retrieval for KB-VQA (DMRAG) is proposed, which re-trieves candidates through multiple retrieval paths that capture complementary visual and semantic cues and performs Question-Adaptive Gated Fusion to balance contributions from different modalities according to the query’s information need.
Zeyu Song, Yimin Deng, Yu-Xin Zhang et al.· Proceedings of the Thirty-Fi...· 0 citations
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