Incremental Generation is Necessity and Sufficient for Universality in Flow-Based Modelling

The exploration of incremental flow-based denoising models highlights their critical role in generative modeling, as discussed in the base article. This aligns with ongoing research in Natural Language Processing (NLP), such as the challenges in Entity Linking and cross-lingual applications, which also emphasize the need for robust theoretical frameworks. The impossibility theorem presented in the base article resonates with the complexities faced in NLP, where the divergence in predicate-argument structures can hinder effective language transfer. Thus, both fields underscore the necessity for foundational advancements to enhance model universality and applicability.