Methods › General › Theorem Proving Models › NeuroTactic
NeuroTactic
Introduced by Zhaoyu Li et al. in Graph Contrastive Pre-training for Effective Theorem Reasoning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
NeuroTactic is a model for theorem proving which leverages graph neural networks to represent the theorem and premises, and applies graph contrastive learning for pre-training. Specifically, premise selection is designed as a pretext task for the graph contrastive learning approach. The learned representations are then used for the downstream task, tactic prediction
Papers archive 2025-07-28
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Graph Contrastive Pre-training for Effective Theorem Reasoning 24 Aug 2021 · 0 repositories · arXiv:2108.10821
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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