MM-CondChain: 一种可编程验证的视觉基础深度组合推理基准
MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning
AI总结：
MM-CondChain提出了一种可编程验证的视觉基础深度组合推理基准，通过多层推理链评估多模态大语言模型在复杂视觉任务中的表现，实验表明深度组合推理仍是重大挑战。
摘要：
多模态大语言模型（MLLMs）越来越多地用于执行视觉工作流，如导航GUI界面，其中下一步取决于经过验证的视觉组合条件（例如，
英文摘要：
Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional conditions (e.g., "if a permission dialog appears and the color of the interface is green, click Allow") and the process may branch or terminate early. Yet this capability remains under-evaluated: existing benchmarks focus on shallow-compositions or independent-constraints rather than deeply chained compositional conditionals. In this paper, we introduce MM-CondChain, a benchmark for visually grounded deep compositional reasoning. Each benchmark instance is organized as a multi-layer reasoning chain, where every layer contains a non-trivial compositional condition grounded in visual evidence and built from multiple objects, attributes, or relations. To answer correctly, an MLLM must perceive the image in detail, reason over multiple visual elements at each step, and follow the resulting execution path to the final outcome. To scalably construct such workflow-style data, we propose an agentic synthesis pipeline: a Planner orchestrates layer-by-layer generation of compositional conditions, while a Verifiable Programmatic Intermediate Representation (VPIR) ensures each layer's condition is mechanically verifiable. A Composer then assembles these verified layers into complete instructions. Using this pipeline, we construct benchmarks across three visual domains: natural images, data charts, and GUI trajectories. Experiments on a range of MLLMs show that even the strongest model attains only 53.33 Path F1, with sharp drops on hard negatives and as depth or predicate complexity grows, confirming that deep compositional reasoning remains a fundamental challenge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
