VCode: a Multimodal Coding Benchmark with SVG as Symbolic Visual
  Representation
Code has emerged as a precise and executable medium for reasoning and action
in the agent era. Yet, progress has largely focused on language-centric tasks
such as program synthesis and debugging, leaving visual-centric coding
underexplored. Inspired by how humans reason over sketches, we advocate SVG
code as a compact, interpretable, and executable visual representation. We
introduce VCode, a benchmark that reframes multimodal understanding as code
generation: given an image, a model must produce SVG that preserves symbolic
meaning for downstream reasoning. VCode covers three domains - general
commonsense (MM-Vet), professional disciplines (MMMU), and visual-centric
perception (CV-Bench). To assess symbolic fidelity, we propose CodeVQA, a novel
evaluation protocol in which a policy model answers questions over rendered
SVGs; correct answers indicate faithful symbolic preservation. Empirically,
frontier VLMs struggle to generate faithful SVGs, revealing a persistent gap
between language-centric and visual-centric coding. To close this gap, we
introduce VCoder, an agentic framework that augments VLMs along two axes: (i)
Thinking with Revision, which iteratively analyzes discrepancies and refines
SVG code; and (ii) Acting with Visual Tools, where detectors and parsers supply
structured cues such as objects, shapes, and text beyond the model's intrinsic
capacity. Across benchmarks, frontier VLMs with strong reasoning capabilities
score well overall yet remain limited in professional knowledge and 3D
reasoning. VCoder delivers a 12.3-point overall gain over the top-performing
Claude-4-Opus. Human studies show that both humans and VLMs perform worse on
rendered SVGs, their consistency reveals the promise of symbolic visual
representation. The benchmark and code are available at
https://github.com/CSU-JPG/VCode.
690aba32d70e173c84528fa7
Kevin Qinghong Lin
claimed_verified
2025-11-05T16:24:42.232Z
64440be5af034cdfd69ca3a7
https://cdn-avatars.huggingface.co/v1/production/uploads/64440be5af034cdfd69ca3a7/qmx24QiDFT29vleCxL9TX.jpeg
Qinghong (Kevin) Lin
KevinQHLin
user
KevinQHLin
690aba32d70e173c84528fa8
Yuhao Zheng
claimed_verified
2026-02-11T11:21:41.750Z
64107c7df52d7eb22e062956
/avatars/7b1cee9a2b8454fedfbd4c3d1df9865c.svg
Yuhao Zheng
yhzheng1031
user
yhzheng1031
690aba32d70e173c84528fa9
Hangyu Ran
claimed_verified
2025-11-05T16:24:40.244Z
67cf130b3bec454b361a6b70
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/XMcVRwcKJtUfY_WAvTsM6.jpeg
Hangyu Ran
hangyuran
user
hangyuran
690aba32d70e173c84528faa
Dantong Zhu
claimed_verified
2025-11-10T09:32:27.802Z
67eb81928ebaba352ed56c3b
https://cdn-avatars.huggingface.co/v1/production/uploads/67eb81928ebaba352ed56c3b/kTV3pDz5UEc9NJ80Gz9_N.jpeg
dt
zhudantong
user
zhudantong
690aba32d70e173c84528fab
Dongxing Mao
claimed_verified
2025-11-10T09:32:29.840Z
6331b4719d77091830d2e0c4
https://cdn-avatars.huggingface.co/v1/production/uploads/6331b4719d77091830d2e0c4/8JXaovCvmBJyvoCVuT3ph.jpeg
Dongxing Mao
neversa
user
neversa
690aba32d70e173c84528fac
Linjie Li
690aba32d70e173c84528fad
Philip Torr
690aba32d70e173c84528fae
Alex Jinpeng Wang
VCode: a Multimodal Coding Benchmark with SVG as Symbolic Visual
  Representation
Code has emerged as a precise and executable medium for reasoning and action
in the agent era. Yet, progress has largely focused on language-centric tasks
such as program synthesis and debugging, leaving visual-centric coding
underexplored. Inspired by how humans reason over sketches, we advocate SVG
code as a compact, interpretable, and executable visual representation. We
introduce VCode, a benchmark that reframes multimodal understanding as code
generation: given an image, a model must produce SVG that preserves symbolic
meaning for downstream reasoning. VCode covers three domains - general
commonsense (MM-Vet), professional disciplines (MMMU), and visual-centric
perception (CV-Bench). To assess symbolic fidelity, we propose CodeVQA, a novel
evaluation protocol in which a policy model answers questions over rendered
SVGs; correct answers indicate faithful symbolic preservation. Empirically,
frontier VLMs struggle to generate faithful SVGs, revealing a persistent gap
between language-centric and visual-centric coding. To close this gap, we
introduce VCoder, an agentic framework that augments VLMs along two axes: (i)
Thinking with Revision, which iteratively analyzes discrepancies and refines
SVG code; and (ii) Acting with Visual Tools, where detectors and parsers supply
structured cues such as objects, shapes, and text beyond the model's intrinsic
capacity. Across benchmarks, frontier VLMs with strong reasoning capabilities
score well overall yet remain limited in professional knowledge and 3D
reasoning. VCoder delivers a 12.3-point overall gain over the top-performing
Claude-4-Opus. Human studies show that both humans and VLMs perform worse on
rendered SVGs, their consistency reveals the promise of symbolic visual
representation. The benchmark and code are available at
https://github.com/CSU-JPG/VCode.
VCode introduces a benchmark for generating SVG code from images to preserve symbolic meaning, highlighting gaps in visual-centric coding and proposing VCoder to improve performance.
SVG VCode multimodal understanding code generation CodeVQA VLMs Thinking with Revision Acting with Visual Tools professional knowledge 3D reasoning VCoder