LEDGERMIND proposes a provenance‑constrained state‑machine view of multimodal agent trajectories for visual question answering. It normalizes tool outputs into a structured evidence ledger, allowing evaluation of whether reasoning is grounded in actual evidence rather than language priors or error cancellation. The framework aims to enhance the reliability of multi‑step perception, retrieval, and reasoning pipelines.