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Jingguang Li

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#artificial intelligence Preprint Sep 2026

CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory

Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length increases. Recent approaches address this by processing input chunk by chunk while maintaining a bounded textual memory in model context. However, premature information...

Jing-Guang Li, Yebo Wu, Zu-Yi Guo et al. · 0 citations

Transcending Memory Constraints in Federated Learning via Sequential Block-Wise Training

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the substantial memory footprint required for model training imposes a critical memory wall on resource-constrained edge devices, severely limiting their participation and compromising the inc...

Yebo Wu, Jing-Guang Li, Chunlin Tian et al. · 1 citation

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