This work presents JITOMA (Just-In-Time On-demand Memory Activation), a closed-loop framework that unifies task reasoning, perception, and memory into a just-in-time growth process, and introduces JITOMA-Bench, a comprehensive suite for long-horizon multi-tasking and complex multi-step reasoning.
Experiments show that this method provides faithful explanations and outperforms state-of-the-art baselines on temporal graph datasets, spanning node property prediction, link prediction tasks and graph classification tasks.
Yazheng Liu, Xi Zhang, Si-Hong Xie et al.· arXiv.org· 0 citations
DRIQN is proposed to integrate Distributionally Robust Optimization (DRO) with implicit quantile networks to optimize worst-case performance under natural environmental conditions and incorporates heterogeneous noise sources and target robustness-critical scenarios.
Zhao-Fan Zhang, Minghao Yang, Si-Hong Xie et al.· 0 citations
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