用草图思考：通过逻辑重建实现光学解压
Thinking with Drafting: Optical Decompression via Logical Reconstruction
AI总结：
本文提出通过逻辑重建实现光学解压，引入TwD方法，利用领域特定语言作为中间表示，以生成可执行代码的视觉证明，建立视觉生成与逻辑验证的闭环系统。
摘要：
现有多模态大语言模型在高保真视觉感知和探索性视觉生成方面取得了进展，但在复杂推理任务中存在精度悖论：光学感知系统无法捕捉逻辑拓扑，而基于像素的生成模型会产生缺乏数学精确性的视觉伪影。为此，我们提出将视觉输入上的推理重新概念化为光学解压——从压缩的视觉标记中重建潜在的逻辑结构。基于解析即推理的公理，我们引入用草图思考（TwD），利用最小化的领域特定语言（DSL）作为中间表示。不同于标准方法直接生成答案，TwD迫使模型将其思维模型转化为可执行代码，生成确定性的视觉证明以进行自我验证。为了验证这一点，我们提出了视觉代数基准（VisAlg）。实验表明，TwD作为更优的认知支架。我们的工作建立了一个闭环系统，其中视觉生成不再是创造性的输出，而是逻辑验证者，为视觉推理提供了一条可推广的路径。
英文摘要：
Existing multimodal large language models have achieved high-fidelity visual perception and exploratory visual generation. However, a precision paradox persists in complex reasoning tasks: optical perception systems transcribe symbols without capturing logical topology, while pixel-based generative models produce visual artifacts lacking mathematical exactness. To bridge this gap, we propose that reasoning over visual inputs be reconceptualized as optical decompression-the process of reconstructing latent logical structures from compressed visual tokens. Guided by the axiom that Parsing is Reasoning, we introduce Thinking with Drafting (TwD), which utilizes a minimalist Domain-Specific Language (DSL) as a grounding intermediate representation. Unlike standard approaches that hallucinate answers directly, TwD forces the model to draft its mental model into executable code, rendering deterministic visual proofs for self-verification. To validate this, we present VisAlg, a visual algebra benchmark. Experiments demonstrate that TwD serve as a superior cognitive scaffold. Our work establishes a closed-loop system where visual generation acts not as a creative output but as a logical verifier, offering a generalizable path for visual reasoning.
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。
