The field of table question answering and retrieval-augmented generation is moving towards more advanced and adaptable methods. Recent developments have focused on improving the ability of large language models to understand and reason about tabular data, with a particular emphasis on multi-modal and domain-specific approaches. This includes the development of new frameworks and techniques for generating domain-grounded question-answer-context triples, as well as methods for detecting errors and inconsistencies in tabular data. Notable papers in this area include: VeritasFi, which presents a groundbreaking framework for multi-modal financial question answering. SG-XDEAT, which proposes a novel framework for supervised learning on tabular data that integrates sparsity-guided cross-dimensional and cross-encoding attention with target-aware conditioning.