MuralAgent: Language-Guided Agentic Inspection for Large-Scale Mural Image Stitching

Large-scale image stitching is essential for the digital preservation of cultural heritage murals, yet stitching artifacts are difficult to identify due to their sparse distribution in images and their visual similarity to intrinsic mural degradations. Existing vision-language models and multimodal agents often rely on fixed or general-purpose visual exploration, making it difficult to efficiently acquire the evidence needed to resolve such ambiguities under a limited inspection budget. We formulate mural stitching inspection as an uncertainty-driven evidence acquisition problem, where an agent must decide not only where to look, but also what evidence to acquire to resolve uncertain observations. We present MuralAgent, a multimodal agent that performs closed-loop inspection by maintaining spatial beliefs and competing hypotheses, adaptively selecting visual observations and hypothesis-conditioned forensic evidence. It further performs source-aware attribution to distinguish stitching-induced artifacts from intrinsic mural degradation. Experiments on MuralSI-Bench show that MuralAgent improves attribution F1 by 11.6 percentage points over the strongest multimodal agent baseline while reducing high-resolution inspection area by 44.3%.