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Topology-Aware Tokenization (TopoTok) is proposed, an item tokenization framework that preserves item relational structure throughout the quantization hierarchy and effectively alleviates topology distortion and consistently outperforms state-of-the-art tokenizers.
This work proposes a two-stage framework to assess the severity of false claims during disasters, and investigates false claim severity assessment as a human-AI alignment problem, evaluating whether models can reproduce human judgments under a shared evaluation rubric rather than merely predicting severity labels.
PropUQ-MAS is proposed, an error propagation-aware UQ framework that represents MAS execution as a communication-structured graph and estimates each step's reliability by combining local uncertainty with uncertainty inherited from upstream messages.
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