Grounding Long Financial Text with Semantically-Enriched Tables via Multimodal Learning
arXiv:2610.37913v1 Announce Type: new Abstract: We propose a multimodal transformer framework for efficiently modeling long1 financial documents that combine structured tabular data with noisy, unstructured2 textual disclosures. The framework represents financial tables as semantically3 enriched token sequences and integrates them with long-form text through a cross-4 attention mechanism that explicitly captures cross-modal dependencies. In par-5 ticular, we augment each numerical variable with






