图像越多，问题越多？视觉语言模型故障模式的受控分析

摘要

大型视觉语言模型（LVLM）已展现出卓越能力，但其在多图像理解与推理方面的熟练度仍亟待探索。现有基准虽已启动对多图像模型的评估，但对其核心缺陷及成因的系统性分析尚属空白。本研究推出MIMIC（多图像模型洞察与挑战）基准，旨在严格评估LVLM的多图像处理能力。通过MIMIC进行的诊断实验揭示出普遍性问题：LVLM常无法跨图像整合信息，且难以同时追踪或关注多个概念。针对这些缺陷，我们提出两项创新性互补方案。在数据层面，我们设计了一种程序化数据生成策略，将单图像标注合成为具有针对性的丰富多图像训练样本；在优化层面，我们通过分析层级注意力模式，推导出专为多图像输入定制的注意力掩码方案。实验表明，该方法显著提升了跨图像聚合能力，并在现有多图像基准测试中超越先前最优成果，实现多任务性能全面提升。相关数据与代码已公开于https://github.com/anurag-198/MIMIC。

English

Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities, yet their proficiency in understanding and reasoning over multiple images remains largely unexplored. While existing benchmarks have initiated the evaluation of multi-image models, a comprehensive analysis of their core weaknesses and their causes is still lacking. In this work, we introduce MIMIC (Multi-Image Model Insights and Challenges), a new benchmark designed to rigorously evaluate the multi-image capabilities of LVLMs. Using MIMIC, we conduct a series of diagnostic experiments that reveal pervasive issues: LVLMs often fail to aggregate information across images and struggle to track or attend to multiple concepts simultaneously. To address these failures, we propose two novel complementary remedies. On the data side, we present a procedural data-generation strategy that composes single-image annotations into rich, targeted multi-image training examples. On the optimization side, we analyze layer-wise attention patterns and derive an attention-masking scheme tailored for multi-image inputs. Experiments substantially improved cross-image aggregation, while also enhancing performance on existing multi-image benchmarks, outperforming prior state of the art across tasks. Data and code will be made available at https://github.com/anurag-198/MIMIC.

图像越多，问题越多？视觉语言模型故障模式的受控分析

More Images, More Problems? A Controlled Analysis of VLM Failure Modes

摘要

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