提出可控制分子生成的新方法，无需重训练即可灵活调控生成结果。
Controlled Generation with Equivariant Variational Flow Matching
- 将流匹配转化为变分推断，实现条件生成的端到端训练与后验控制
- 在未控生成上达顶尖水平，在可控生成中优于现有模型
- 支持旋转、平移、排列对称性，适合化学分子等对称结构生成
我们在变分流匹配（VFM）框架内推导出可控生成的目标，将流匹配视为变分推断问题。我们证明可控生成可通过两种方式实现：(1) 条件生成模型的端到端训练，或 (2) 作为贝叶斯推断问题，实现对无条件模型的后验控制而无需重新训练。此外，我们建立了等变生成的条件，并提出了专为分子生成设计的等变VFM形式，确保对旋转、平移和排列的不变性。我们在无条件与可控分子生成任务上进行了评估，未控生成达到当前最优性能，可控生成在端到端训练与贝叶斯推断设置下均优于现有先进模型。该工作强化了基于流的生成模型与贝叶斯推断之间的联系，提供了一个可扩展且原理严谨的约束驱动与对称性感知生成框架。
原文摘要 · Abstract (English)
We derive a controlled generation objective within the framework of Variational Flow Matching (VFM), which casts flow matching as a variational inference problem. We demonstrate that controlled generation can be implemented two ways: (1) by way of end-to-end training of conditional generative models, or (2) as a Bayesian inference problem, enabling post hoc control of unconditional models without retraining. Furthermore, we establish the conditions required for equivariant generation and provide an equivariant formulation of VFM tailored for molecular generation, ensuring invariance to rotations, translations, and permutations. We evaluate our approach on both uncontrolled and controlled molecular generation, achieving state-of-the-art performance on uncontrolled generation and outperforming state-of-the-art models in controlled generation, both with end-to-end training and in the Bayesian inference setting. This work strengthens the connection between flow-based generative modeling and Bayesian inference, offering a scalable and principled framework for constraint-driven and symmetry-aware generation.
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