SiST-GNN performs simultaneous spatial-temporal message passing on a temporally augmented graph and reports 109-277% gains in fixed-split dynamic link prediction over prior methods.
Williams and Jing Peng
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A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.
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'Si'multaneous 'S'patial-'T'emporal Message Passing for Dynamic Graph Representation Learning
SiST-GNN performs simultaneous spatial-temporal message passing on a temporally augmented graph and reports 109-277% gains in fixed-split dynamic link prediction over prior methods.
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Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
A recurrent-depth architecture enables language models to improve reasoning performance by iterating computation in latent space, achieving gains equivalent to much larger models on benchmarks.