A geometry-adaptive Riemannian framework for molecular representation learning, which explicitly models motifs as the basic units and learns their embeddings across multiple constant-curvature spaces, offering a general and scalable framework for scientific molecular modeling.
Fei Liu, Wen-Kai Lu, Fei-Long Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Molecular properties are often governed by a small number of local substructures, or motifs, whose topologies can vary drastically across molecules. Existing molecular representation learning approaches typically embed all motifs into a single Euclidean or fixed-curvature space, which fails to capture the motif-level t...
Fei Liu, Wen-Kai Lu, Feilong Wang et al.· Proceedings of the 32nd ACM...· 0 citations
LeanMem is proposed, a lightweight long-term memory framework that improves accuracy over the strongest memory-based baseline in every setting, at the lowest or near-lowest construction cost, inference tokens, and latency.
Yuxin Liao, Le Wu, Min Hou et al.· 4 citations
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