回答之前先解释：组合视觉推理综述
Explain Before You Answer: A Survey on Compositional Visual Reasoning
AI总结：
综述2023年至2025年组合视觉推理文献，形式化核心定义，追溯范式转变，编目基准指标，提炼见解、识别挑战并概述方向，为该领域提供统一分类、历史路线图和批判性展望。
摘要：
组合视觉推理已成为多模态人工智能的关键研究前沿，旨在赋予机器类似人类的能力，以分解视觉场景、锚定中间概念并进行多步逻辑推理。早期综述聚焦于整体视觉语言模型或一般多模态推理，缺少对组合视觉推理文献的专门综合。本文对2023年至2025年的文献进行全面综述，系统回顾260多篇顶级会议论文。首先形式化核心定义，阐述组合方法优势；接着追溯五阶段范式转变；然后编目60多个基准和指标；最后提炼关键见解，识别开放挑战并概述未来方向。
英文摘要：
Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research.
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。
