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<a href="https://arxiv.org/abs/2509.24728">Beyond Softmax: A Natural Parameterization for Categorical Random Variables</a> (Manenti and Alippi) and
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<a href="https://arxiv.org/abs/2507.23604">Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning</a> (Marzi et al).
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Check them out!
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- date: 2025/10
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text: >
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We have released two new preprints about
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<a href="https://arxiv.org/abs/2508.03283">Online Continual Graph Learning</a> (OCGL, Donghi et al) and
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<a href="https://arxiv.org/abs/2510.06819">The Unreasonable Effectiveness of Randomized Representations in OCGL</a> (Donghi et al).
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Take a look!
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- date: 2025/09
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text: 'Our papers <a href="https://arxiv.org/abs/2506.15507">Over-squashing in Spatiotemporal Graph Neural Networks</a> (Marisca et al.) and <a href="#">Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games</a> (Lu et al.) have been accepted at <strong><a href="https://neurips.cc">NeurIPS 2025</a></strong>!'
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