Towards explainable language reasoning via multi-modal knowledge graphs

The increasing complexity of natural language reasoning in artificial intelligence necessitates a shift from opaque, black-box processing to transparent, interpretable decision-making. While large language models (LLMs) demonstrate remarkable generative capabilities, their reasoning pathways often remain implicit, which limits auditability in high-stakes settings. To address this issue, we propose a framework for explainable language reasoning grounded in Multi-Modal Knowledge Graphs (MMKGs). Our approach unifies textual, visual, and structural knowledge in a shared graph representation, enabling the system to anchor inference in explicit semantic relations rather than in latent correlations alone. We introduce a planner-executor design in which an LLM generates constrained symbolic plans and a deterministic graph engine executes them to return both an answer and a replayable explanation subgraph. Empirically, the framework is competitive on six textual and multimodal benchmarks, reaching 79.8% Hits@1 on WebQSP and 49.3 accuracy on OK-VQA under the protocol used in this study. We deliberately report the main quantitative comparisons as point estimates and interpret small margins conservatively. The principal contribution of the work is therefore the MMKG-grounded reasoning architecture, its operationally auditable explanation mechanism, and the accompanying clarification of what can and cannot be claimed theoretically about explanation faithfulness.