Hasse Diagrams for Attention: A Partial Order Framework for Designing Transformer Masks

Abstract:During the training of large Transformer models, attention masks regulate the scope and direction of information flow across a sequence. Numerous mask variants exist, and operators such as FlexAttention already support arbitrary attention masks. Nevertheless, a systematic formal analysis of the information-flow structure induced by arbitrary masks has been missing. This paper develops a complete theoretical framework. We prove that, with sufficient depth, the information flow of a multi-layer Transformer converges to a Hasse diagram -- a directed acyclic graph representing a partial order. Building on this, we recast the design of parallel training tasks as the problem of finding a minimal common supergraph of Hasse diagrams, and we establish a criterion for the minimal common supergraph. This yields a constructive method to derive attention masks directly from a family of tasks. Applying the framework, we design two novel masks: a block-generation attention mask that ensures training-inference consistency (Block Two-Stream Attention), and a fully supervised bidirectional attention mask (Butterfly Attention). These results demonstrate the framework's capacity to discover new structures.

21 pages, 9 figures. Theoretical framework for attention mask design; no experiments included

Machine Learning (cs.LG)

arXiv:2606.09951 [cs.LG]

(or arXiv:2606.09951v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.09951 Focus to learn more arXiv-issued DOI via DataCite

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