Memory-augmented agents maintain compact user profiles throughout extended conversations, enabling personalized and consistent responses without the need to process the entire dialogue history. The quality of these user profiles relies on the underlying memory management strategy: at each step, the agent must determine...
Yu-Peng Han, Shuo-Chen Liu, Kai Zhang et al.· 2 citations
FairDiff is proposed, a plug-and-play fairness-aware diffusion framework that achieves state-of-the-art performance while effectively mitigating the self-reinforcing Matthew Effect, highlighting its value as a general framework for DRMs.
Song-Li Wu, Xian-Quan Wang, Zhao-Cheng Du et al.· 0 citations
FineSID is a unified quantization framework that moves beyond Top-1 assignment by enabling fine-grained gradient propagation across the entire codebook, effectively alleviating SID collisions and stabilizing training in large, high-dimensional codebooks.
Song-Li Wu, Wei-Nan Gan, Zhao-Cheng Du et al.· 0 citations
This work proposes Adaptive Evidential Multimodal Representation Learning (AEMRL), a framework aligned with this multi-level view of uncertainty that consistently enhances task accuracy, reduces calibration error, and improves robustness to noise, semantic conflict, and missing modalities.
Yu-Peng Han, Kai Zhang, Xian-Quan Wang et al.· Proceedings of the Thirty-Fi...· 0 citations
AD-Reranker is proposed, a novel framework that shifts reranker training from proxy imitation to answer-driven utility optimization, and reformulate the reranker as an environment-grounded agent that interacts with a downstream reader, modeled as a deterministic environment.
Keyu Zhu, Shuanghong Shen, Xianquan Wang et al.· Annual International ACM SIG...· 0 citations
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