BeaconChart: Verifier-Guided Corrective Instruction for Chart Understanding

Chart question answering remains challenging because small errors in evidence extraction and visual grounding can easily propagate into incorrect answers. Although recent methods improve chart understanding, most still rely on one-pass answer generation without explicitly verifying whether the extracted evidence is accurate, and verifier feedback rarely persists beyond the current example. We propose $\textbf{BeaconChart}$, a verifier-guided multi-agent framework that improves chart understanding by accumulating verifier feedback as corrective instruction. Rather than directly trusting an initial prediction, BeaconChart first plans the required evidence, performs chart-aware extraction, and verifies whether the predicted answer is correctly grounded in the chart. When verification fails, it converts the feedback into reusable guidance that improves future inference on similar chart failures. Experiments on ChartQA and ChartX show that BeaconChart consistently outperforms both general-purpose multimodal large language models and recent chart-specialized baselines.