arXiv Machine Learning By Jiaqing Xie, Yuxin Wang, Xipeng Qiu

$\text{GSF-}\chi$: Global Stereochemical Fields for Chiral Graph Transformers

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The paper introduces GSF-χ, a graph transformer that incorporates global stereochemical fields to handle chiral molecules. Unlike traditional models that focus on a single atom, GSF-χ modulates all pairwise interactions using stereogenic units, producing a reflection‑even phase field and a handedness pseudoscalar that guide relative rotations in latent query–key blocks. The authors prove the operator’s even–odd decomposition and demonstrate that GSF-χ improves central‑ECD, axial Rotation, and Symbol metrics over strong baselines, while maintaining enantiomer‑pair consistency through a C₂ projection.

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arXiv Machine Learning
Aug 28

Gromov-Monge Flow Matching for Equivariant Graph Generation

The paper introduces Gromov-Monge Flow Matching, a method that incorporates permutation-equivariance into generative graph models by aligning graph pairs up to node relabeling using the Gromov–Monge distance. It shows theoretically that quotient couplings can be lifted to aligned representatives without extra cost and that symmetrization yields equivariant flow-matching minimizers, even for categorical endpoints. Practically, the authors build minibatch couplings with Gromov–Wasserstein relaxations and optional outer assignments, improving sample quality in continuous graph and categorical molecular generation while remaining compatible with standard equivariant architectures.

By Moritz Piening, Christian Wald