Researchers introduced GRAB, a new approach to multi-table question answering that uses a graph-based encoder and a latent bridge to connect a large language model (LLM) to relational data. The system lifts relational data into a graph, passes messages through it, and uses a small set of query-conditioned tokens to transfer the signals to the LLM. This allows the LLM to reason about the data while maintaining its general capabilities. The model is trained efficiently by only updating the graph encoder and latent bridge, and it outperforms previous methods on multi-table question answering tasks.