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cs.AI2026
History Replays Itself: Unleashing Long-Context Capability in Linear Attention
Li Nanbo, Firas Laakom, Yilan Zhang +10
Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support ongoing computation and preserve…
cs.AI2025
Huxley-Gödel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine
Wenyi Wang, Piotr Piękos, Li Nanbo +5
Recent studies operationalize self-improvement through coding agents that edit their own codebases. They grow a tree of self-modifications through expansion strategies that favor h…
cs.AI2024
FACTS: A Factored State-Space Framework For World Modelling
Li Nanbo, Firas Laakom, Yucheng Xu +2
World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such…