L-TQA: A Modular Multi-Agent Architecture for Context-Constrained Tabular Question Answering - Alves, Helen B.; Fernandes, Diego D.; Dias, Gustavo A. G. S.; Cavalcanti, João A. M. B.; Pinto, Antônio Í. M.; Silva, Lara S.; Santos, Rafael S.; Silva, Thalyson G. N.; Rocha, Cleilton L.; Costa, Leonardo F. da; Campos, Gustavo A. L.; Barros, Ana Luiza B. P. (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
Question Answering over Tabular Data requires accurate numerical reasoning and robust schema interpretation, both of which remain challenging for restricted-parameter language models. This paper presents Laura Tabular Question Answer (L-TQA), a multi-agent architecture designed to mitigate context limitations by decoupling semantic interpretation, schema filtering, and executable code generation. The proposed system improves query synthesis through targeted contextual constraints, combining a response-type classifier to enforce output formats with a dynamic column selector to reduce schema noise. Evaluated on the SemEval-2025 Task 8 DataBench benchmark, L-TQA establishes a new state-of-the-art for models with up to 9 billion parameters, achieving 77.01% accuracy on the test split. In addition, the proposed modular design generalizes effectively to larger proprietary models, highlighting its structural robustness and practical value for privacy-preserving, data-driven applications.