Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding

Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hallucination, attention leakage, and failures in object constancy. Existing approaches typically rely on expensive human annotations or large-scale chain-of-thought (CoT) data generation to solve these challenges. In contrast, we propose Compositional Grounded Contrast (CGC), a low-cost full framework for boosting fine-grained multi-image understanding of MLLMs. Built on existing single-image grounding annotations, CGC constructs compositional multi-image training instances through Inter-Image Contrast and Intra-Image Contrast, which introduce unrelated visual distractor contexts for cross-image discrimination and correlated cross-view samples for object constancy, respectively. CGC further introduces a Rule-Based Spatial Reward within the GRPO framework to improve source-image attribution, spatial alignment, and structured output validity. Experiments show that CGC achieves state-of-the-art results on fine-grained multi-image benchmarks, including MIG-Bench and VLM2-Bench. The learned multi-image capability also generalizes to broader multimodal reasoning tasks, yielding consistent gains on MathVista (+2.90), MuirBench (+2.88), MMStar (+1.93), MMMU (+1.77), BLINK (+1.69) and Hallusion (+1.16) over the Qwen3-VL-8B.