Chain-of-Change Detection for Multimodal Large Language Models

Conventional remote-sensing change detection (CD) models typically follow a common design: Siamese encoders extract features from each date, an interaction module compares them, and a task-specific decoder produces a single output. We refer to this observe-then-compare structure as the CD-specific inductive bias. In practice, these models remain task-specific: binary change detection, semantic change detection, and change captioning are typically addressed by separate models. Multimodal large language models (MLLMs) unify these tasks in a single model, yet retain this bias only through encoder-side feature interaction. Within the LLM, supervision defines the target outputs but leaves observation and comparison implicit, allowing shortcut learning and disagreement between a model's masks and captions about the same change. To address this, we introduce Chain-of-Change Detection (CoCD), which formulates each step of the CD process as an explicitly supervised task and passes the decoded prediction of each stage to the next as evidence. CoCD first observes each date by predicting semantic masks for the queried classes, then compares the image pair to localize change guided by these masks, and finally describes the change from the predicted change mask and its semantic transitions. CoCD connects these stages with two proposed modules, the Semantic State Painter routing the observed semantic states to comparison and the Change Evidence Injector routing the localized change and its transitions to description, extending the CD-specific inductive bias beyond encoder-side feature interaction. Since no existing benchmark provides all three outputs for the same queried classes, we construct CoCD-Dataset from HRSCD and WUSU with aligned per-date semantic masks, change masks, and grounded captions. Experiments show improved change localization, caption quality, and cross-output consistency over unified baselines and a sequencing-only control. Inference-time interventions further demonstrate that downstream predictions use the routed evidence. Code is available at https://anonymous.4open.science/r/cocd_release-3CE3/.