Author

Yi-Lun Wu

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#artificial intelligence Preprint Jun 2026

HyperWorld: Hypergraph-Structured State Serialization Improves Learned Textual World Models

HyperWorld, a controlled study of state serialization for learned textual world models, shows that higher-order state organization is a simple but effective inductive bias for learned symbolic world models, especially when model capacity is limited or test environments differ from training.

Yun-Jian Zhang, Chen-Wei Liang, Tian-Yi Zhang et al. · 0 citations

Open access Jul 2026

Uncertainty-Guided Adaptive Knowledge Distillation for Lightweight Cross-Domain Object Detection

This article introduces Uncertainty-Guided Adaptive Knowledge Distillation (UGAKD), a novel framework designed to enhance UDA while simultaneously reducing model size through targeted knowledge distillation and proposing a two-stage, difficulty-aware training scheme to facilitate learning.

Wei-Lun Tseng, Yi-Lun Wu, Yung-Hui Li et al. · ACM Transactions on Intellig... · 0 citations