Thinking with Drafting: Optical Decompression via Logical Reconstruction
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.
698eb9d9cace060ff123aea4
Jingxuan Wei
698eb9d9cace060ff123aea5
Honghao He
698eb9d9cace060ff123aea6
Caijun Jia
698eb9d9cace060ff123aea7
Siyuan Li
claimed_verified
2026-02-13T09:36:19.311Z
640f7083208821a59b74c757
https://cdn-avatars.huggingface.co/v1/production/uploads/1678735253848-640f7083208821a59b74c757.jpeg
Siyuan Li
Lupin1998
user
Lupin1998
698eb9d9cace060ff123aea8
Zheng Sun
698eb9d9cace060ff123aea9
Yuhang Xu
claimed_verified
2026-02-17T15:51:13.635Z
691ac24a4f16b95a50f482bb
/avatars/4200eb1c92f67b24b7f29007e9105661.svg
Yuhang Xu
Dracozzz
user
Dracozzz
698eb9d9cace060ff123aeaa
Yuanyuan Lin
698eb9d9cace060ff123aeab
Linzhuang Sun
698eb9d9cace060ff123aeac
Yuchen Wu
698eb9d9cace060ff123aead
Bihui Yu
698eb9d9cace060ff123aeae
Xiangxiang Zhang
698eb9d9cace060ff123aeaf
Cheng Tan
claimed_verified
2026-02-13T09:36:16.992Z
64be296a46cc3cdfbb057f7e
https://cdn-avatars.huggingface.co/v1/production/uploads/64be296a46cc3cdfbb057f7e/jSHeNY2AcPifCZzJyFhr4.jpeg
Cheng Tan
chengtan9907
user
chengtan9907
Thinking with Drafting: Optical Decompression via Logical Reconstruction
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.
Visual reasoning is enhanced by reconstructing logical structures from compressed visual tokens through a DSL-based approach that generates deterministic visual proofs for verification.
multimodal large language models visual perception visual generation optical decompression visual tokens Domain-Specific Language visual algebra benchmark visual reasoning logical topology deterministic visual proofs