arXiv:2609.33399v2 Announce Type: replace Abstract: In realistic education, a solution is often expressed not only in words but in a drawing--a circuit, a geometric construction, a function plot--and...
arXiv:2606. 15693v1 Announce Type: cross Abstract: LLMs have significantly advanced code generation, enabling the synthesis of functional programs.
Structural fidelity is essential to scientific methodology diagrams. To communicate research logic, these diagrams must faithfully render components, directional relations, and textual annotations.
arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.
arXiv:2607. 18091v1 Announce Type: cross Abstract: Structural fidelity is essential to scientific methodology diagrams.
TikZilla is a new approach to generating TikZ code from textual descriptions, built on a larger, higher‑quality dataset called DaTikZ‑V4 that includes LLM‑generated figure descriptions. The method uses a two‑stage pipeline: supervised fine‑tuning of small Qwen models (3B and 8B) followed by reinforcement learning with an image encoder that provides semantically faithful reward signals. Human evaluations show that TikZilla outperforms its base models by 1.5–2 points on a 5‑point scale, beats GPT‑4o by 0.5 points, and matches GPT‑5 in image‑based tests while remaining much smaller.
The paper presents a systematic study of scientific image synthesis, comparing pixel‑based generation and programmatic approaches. It introduces ImgCoder, a logic‑driven framework that follows an "understand‑plan‑code" workflow to enhance structural precision, and SciGenBench, a benchmark that evaluates images for information utility and logical validity. The authors find that pixel‑based models exhibit systematic failure modes and that fine‑tuning large multimodal models on rigorously verified synthetic images consistently improves downstream reasoning performance.
arXiv:2610.20538v1 Announce Type: cross Abstract: Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is bu...
arXiv:2610.10066v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation. However, existing...
Vision2Code is a new benchmark for image-to-code generation that covers 15 domains and 539 test examples without relying on reference source code. It evaluates generated programs by executing them and comparing rendered outputs to the original images using domain‑specific rubrics, yielding scores that correlate better with human judgments than generic metrics. The study shows domain variability in model performance and demonstrates that the scoring method can improve fine‑tuning of models like Qwen3.5‑9B and Phi‑4‑MM via rejection sampling.
VinciCoder is a unified framework for multimodal code generation that addresses the limitations of single-task models by training on a large-scale curated corpus of 1.3 M direct generation pairs and 300 k visual‑refinement tasks. It introduces a coarse‑to‑fine Visual Reinforcement Learning (ViRL) approach that uses visual similarity across multi‑scale patches to provide an implementation‑agnostic reward, improving alignment between rendered outputs and input visuals. Experiments on diverse benchmarks show VinciCoder outperforms existing methods, and ablation studies confirm the effectiveness of ViRL.
arXiv:2606. 10334v1 Announce Type: new Abstract: Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are executed by non-differentiable renderers, committing to code before observing the render.