Multi-Granularity Distillation of Intervention Sensitivity Representations for Multimodal Large Language Models

Existing knowledge distillation methods for multimodal large language models (MLLMs) mainly align output distributions or intermediate representations, but rarely supervise how predictions depend on local visual evidence. This limitation may hinder the transfer of fine-grained perception and compositional reasoning. We propose Causal and Multi-source Visual Attribution Distillation (CMVAD), which transfers teacher-derived visual evidence during distillation. Region-level interventions first estimate token-level causal visual attributions, identifying image regions that influence each generated token. The Causal Evidence Adapter Fetch (CEAF) module jointly models token importance and semantic interactions to adaptively aggregate multi-source causal evidence into a unified representation. A Multi-granularity Attribution Distillation (MAD) objective transfers this representation via global evidence distribution alignment, critical-region alignment, and evidence-ranking consistency. Experiments on general visual question answering and compositional reasoning benchmarks show that CMVAD outperforms existing MLLM distillation methods, achieving larger gains in compositional reasoning while retaining competitive visual question answering performance. Results across teacher–student architectures demonstrate its generalizability. We further evaluate VisDrone UAV object detection through overall comparisons, providing supplementary evidence of CMVAD’s applicability to object recognition and localization in UAV imagery.