This work proposes AQUA, which trains agents to actively query information-rich regions through Gaussian Splatting environments, transforming global lossy compression into local lossless localization with targeted spatial queries.
Chonghan Gao, Haoyi Zhou, Zhemeng Luo et al.· Proceedings of the 32nd ACM...· 0 citations
This tutorial provides a systematic overview of LLM personalization, covering core paradigms such as user-aware representations, prompt-based adaptation, memory augmentation, parameter-efficient fine-tuning, and continual learning.
Rui-Jie Wang, Qing-Kai Zeng, Xue-Fei Wang et al.· Proceedings of the 32nd ACM...· 0 citations
PAC-Bayes-regularized Meta-LoRA is proposed, which uses a meta-learned LoRA initialization as both the adaptation start and prior center, while adjusting update strength according to support-set size and predictive uncertainty to limit overfitting under sparse or ambiguous evidence.
Xuefei Wang, Jun Han, Zi-Xuan Wang et al.· 0 citations
LatentFlow is proposed, a novel framework that treats channel dependency evolution as a latent continuous dynamic process using an Ornstein-Uhlenbeck (O-U) process, allowing the model to capture smooth dependency shifts while maintaining robustness against structural noise.
Lijun Sun, Shuai Zhang, Xin Xue et al.· Proceedings of the 32nd ACM...· 0 citations
This workshop explores how graph learning can support emerging challenges in knowledge reasoning, temporal and multi-hop inference, and AI systems and investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI system...
Qing-Yun Sun, Zi-Wei Zhang, Xing-Cheng Fu et al.· Proceedings of the 32nd ACM...· 0 citations
Recent advances enable LLMs to generate simulation code from natural language, yet interpreting 3D physical field outputs remains unsolved. Existing 3D scene compression methods fail on physical fields due to absent semantic grounding and information loss. We discover that typical physical fields exhibit extreme inform...
Chonghan Gao, Haoyi Zhou, Zhemeng Luo et al.· Proceedings of the 32nd ACM...· 0 citations
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