JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence
Authors: Qiushi Sun
Jingyang Gong
Yang Liu
Qiaosheng Chen
Lei Li
Kai Chen
Qipeng Guo
Ben Kao
Fei Yuan
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal LLM
Data Synthesis
Code Generation
Data Visualization
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The scope of neural code intelligence is rapidly expanding beyond text-based source code to encompass the rich visual outputs that programs generate. This visual dimension is critical for advanced applications like flexible content generation and precise, program-driven editing of visualizations. However, progress has been impeded by the scarcity of high-quality multimodal code data, a bottleneck stemming from challenges in synthesis and quality assessment. To address these challenges, we make contributions from both a data and modeling perspective. We first introduce a complete synthesis toolkit that leverages reciprocal synergies between data modalities to efficiently produce a large-scale, high-quality corpus spanning from standard charts to complex interactive web UIs and code-driven animations. Leveraging this toolkit, we construct JanusCode-800K, the largest multimodal code corpus to date. This powers the training of our models, JanusCoder and JanusCoderV, which establish a visual-programmatic interface for generating code from textual instructions, visual inputs, or a combination of both. Our unified model is a departure from existing approaches that build specialized models for isolated tasks. Extensive experiments on both text-centric and vision-centric coding tasks demonstrate the superior performance of the JanusCoder series, with our 7B to 14B scale models approaching or even exceeding the performance of commercial models. Furthermore, extensive analysis provides key insights into harmonizing programmatic logic with its visual expression. Our code and checkpoints are available at \url{https://github.com/InternLM/JanusCoder}.

Published as a conference paper at ICLR 2026
JANUSCODER: TOWARDS A FOUNDATIONAL VISUAL- PROGRAMMATIC INTERFACE FOR CODE INTELLIGENCE
Qiushi Sun 96t Jingyang Gong 0 Yang Liu
Qiaosheng Chen Lei Li Kai Chen ?
Qipeng Guo Ben Kao Fei Yuan
The University of Hong Kong Shanghai AI Laboratory SNanjing University
*Camegie Mellon University Shanghai Innovation Institute
qi gsui sunê connect. hku. hk, j jingyang- inggangg.onngnnu..... edu qschen&smal 1 edu. cn, y1 fu20..0.. n nju@gmail com, lei 1iiiliee cecs. cmu. cmu.edu kaoßes hku, hk, (chenkai, guoqi peng, yuanfei @pj ab. org. cn
ABSTRACT
The scope of neural code intelligence is rapidly expanding beyond text-based source code encompass the rich visual outputs that programs generate. This visual dimension is critical for advanced applications like flexible content generation and precise, program-driven editing of visualizations. However, progress has been im- peded by the scarcity of high-quality multi-modal code data, a bottleneck stemming from challenges in synthesis and quality assessment. To address these challenges, we make contributions from both a data and modeling perspective. We first in- troduce complete synthesis toolkit that leverages reciprocal synergies between data modalities to efficiently produce a large-scale, high-quality corpus spanning from standard charts to complex interactive web UIs and code-driven animations. Leveraging this toolkit, code corpus to date. This powers the training our models, JANUSCODER and JANUSCODERV which establish risual-programmate interface for generating code from textual instructions, visual inputs, or a combination of both. Our unified model is departure from existing approaches that build specialized models for isolated tasks. Extensive experiments on both text-centric and vision-centric cod- ing tasks demonstrate the superior performance of the JANUSCODER series, with our 7B to 14B scale models approaching or even exceeding the performance of commercial models. Furthermore, extensive analysis provides key insights into har- monizing programmatic logic with its visual expression. Our code and checkpoints are available ht : / gigh........... com Int InternLM/ I JanusCoder.
construct JANUSCODE-8000
the largest multimodal
INTRODUCTION
The advent of Large Language Models (LLMss; Hurst et al., 2024; Anthropic AI, 2024) has signifi- cantly advanced the field of code intelligence (Sun et al., 2024a), revolutionizing tasks centered on textual source code. Building on this, the scope of code intelligence naturally expands beyond text to encompass the rich and diverse visual manifestations that programs generate (Gemini Team, 2025; Si gap. Establishing a generalist
et al., 2025), with the aspiration of bridging the perceptual-symbolt modeling interface that harmonizes code's logic with its visual expression therefore the next frontier. Such an interface would empower models to flexibly generate data visualizations (Galimzyanov et al., 2025; Ni et al., 2025) and interactive front-ends (Chen al., 2025a;b), replicate precisely edit visual artifacts from multimodal inputs (Yang et al., 2025b; Xia et al., 2025), and even build complex, code-driven animations (Ku al., 2025) elucidate a concept like "Attention Is All You Need".
Despite its promise, the connection between code and vision remains in its early stages. While recent models have shown success in handling unimodal symbolic representations (Xu et al., 2024b), extending this to multimodal scenarios presents far greater challenges. The first challenge lies at the modeling level. Current research predominantly focuses on program-aided understanding (Qiu et al.,
Equal contribution ø Corresponding author !Project lead

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Figure I: JANUSCODER is a suite of models that establishes a unified visual- poouall-ogrammatic interface, advancing multimodal code intelligence. It supports diverse tasks by combining code with visual content generation, editing, and interpretation in a unified manner.
2025; Chen et al., 2025c) and reasoning (Surís al., 2023; Guo et al., 2025), while fine-grained perception (Liu et al., 2025) and generative capability remain significantly underdeveloped (Wang al., 2025a). For the few well-explored scenarios (Wang et al., 2024; Yun al., 2024), existing works often build specialized models for isolated targets (e.g., one for chart-to-code, another for WebUl-to-code), leading to models that can neither generalize across scenarios nor scale effectively.
Second, and more fundamentally, progress is impeded by the scarcity of high-quality, diverse multimodal code data. The heterogeneity of content existing corpora (Gui al., 2025; Ni et al., 2025) presents significant challenge, along with varying data richness across different programming languages (PI.s), diverse styles of natural language (NI.) instructions, and the vast array of visual outputs that code can produce. For instance, these visual outputs range from static Matplotlib charts and interactive WebUIs to extended animations in the style of 3Blue Brown'. Creating a comprehensive corpus that covers this spectrum is a formidable task. It requires not only large-scale data collection and processing but also well-matched validation environments (e.g., computation / rendering engines), and rigorous quality control over the diverse visual contents.
In this work, we are motivated to build a unified model to facilitate the development of multimodal code intelligence. Toward this goal, we make the following contributions:
1. We develop and release a versatile data synthesis toolkit. This enables the automatic synthesis of multimodal code data across heterogeneous domains and PLs, including but not limited to charts, Web UIs, visual artifacts, and code-driven animations. By doing so, it significantly reduces the engineering efforts required for data curation in future research.
2. Building on this data toolkit, we curate JANUSCODE-800K, the largest multimodal code intelli- gence corpus date. Notably, our corpus includes large-scale animation and artifact data that have not been present in previous works.
3. With the above data innovations and by fostering synergies across different modalities and tasks, we developed JANUSC OODER and JANUSCODERV. As illustrated in Figure 1, these models constitute a unified interface designed to tackle a broad spectrum of visual programmatic tasks.
4. We present a comprehensive evaluation, covering seven established and newly proposed bench- marks. Our models demonstrate superior performance improvements in both text-centric and vision- centric settings, approaching or even exceeding the performance of leading commercial models. This indicates that the JANUSCODER series can serve as a strong open-source foundational model for future research and applications.
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2 RELATED WORKS
Code Generation for Visual Interfaces. LLMs have been widely explored for text-centric code generation of visual interfaces, including data visualizations (Yang al., 2024; Zhuo et al., 2025b), web pages (Chen et al., 2025d), and interactive UIs (Chen et al., 2025a). Early efforts focused on Python libraries (e.g.. Matplotlib, Seaborn) for producing figures in scientific workflows (Zhang et al., 2024b; Sun et al., 2025b). Later work extended to chart generation and editing (Zhao et al., 2025a), and to mapping NI. instructions into web-based artifacts (Zhang al., 2025) or structured UI interactions (Cheng et al., 2024; Sun et al., 2024b). Overall, these approaches highlight the potential of L.LMs to author executable visual content, though they remain constrained to text-driven inputs.
Iisualy-Groundee Code Generation and Understanding. Another line of work emphasizes multimodal inputs (vision-centric), where models interpret visual information to produce or reason about symbolic code (Hu et al., 2024; Jiang al., 2025). Representative efforts include chart understanding, which evaluates the extraction of structured knowledge from plots (Masry et al., 2022; Zhang al., 2024a), and chart-to-code generation, which requires reproducing scientific plots from images with captions or instructions (Zhao et al., 2025b; Xia et al., 2025; Wu et al., 2025). Beyond charts, studies extend to theorem visualization (Ku et al., 2025), multimodal algorithmic problem solving (Li et al., 2024), and structured vector graphics such as SVGs (Yang et al., 2025c; Nishina & Matsui, 2024). While these works demonstrate progress, they largely target isolated domains and modalities. In contrast, we move beyond these constraints by unifying diverse domains and modalities across charts, web UIs, amimations, symbolic computation, and more, taking leap forward in advancing multimodal code intelligence.
METHOD
To empower models for multimodal code intelligence, propose versatile data toolkit that incorporates model interactions (Sun et al., 2024c) and compiler feedback to tackle multifaceted demands. In contrast to prior data approaches, which often suffer from lack of instruction diversity, scarcity in specialized domains, and insufficient validation for visual-code alignment, our pipeline establishes principled workflow. As shown in Figure 2: (I) Data Sourcing. where raw assets are collected and categorized; (2) Data Synthesis & Curation, where new instruction-cod pairs is generated and refined through a multi-strategy engine; and (3) Quality Control, which ensures data fidelity through automated validation and LLM/VLM judging.
Collection and Categorization
Curation
Reward Modeling
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Figure An overview of our toolkit for curating JANUSCODE-SOOE -80K, which integrates heterogeneous data sourcing, multi-strategy synthesis and curation, and LLM/VLM-based reward modeling after execution checks.
3.1 DATA COLLECTION
Our pipeline begins by aggregating raw data from a vast and heterogeneous sources. These include large-scale public datasets (e.g., VtackV2; Lozhkov et al., 2024), extensive web corpora (e.g., Web-
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Table Overview of the strategies used to construct JanusCode Data from multiple sources, different
colored squares represent different strategies: Guided Evolution;
Re-contextualizztion:
Reverse Instruction; Bidirectional Translation.
Source Data Type
Size
Validation
Reward
Strategics
Maiplotlib
200K
Python
VLM
Charts
77K
Python
VLM
Algorithm
100K
Python
VLM
Mathematica
11K
Wolfram Engine
LLM
Animation
5K
Python Manim Engine
VLM
Scientific PLs
400K
LLM
SVG
400K
VLM
WebUI
270K
Playwright
VLM
General Artifacts
10K
Playwright
VLM
Scientific demonstration
10K
Playwright
VLM
Code2M; Gui al., 2025), specialized knowledge bases like the Wolfram Demonstrations Project, and competitive programming problems (Xu et al., 2025; Sun et al., 2025a). All sourced data is then classified into two primary formats:
Paired Data (Dpiree) ): Datasets containing instruction-code pairs (L,C). When visual output is available, it is included as an optional component, forming triplet ( I,C,V V).
Code-Only Data (Deode y: Unlabeled datasets consisting solely of code snippets, denoted as C.
A significant challenge within Deade is the form,f complex code files, such as single Manim script that generates a 5-minute-long mathematical animation. Such monolithic files contain numerous distinct conceptual steps but are not structured for direct learning. To address this, we employ sophisticated decomposition strategy utilizing Abstract Syntax Trees (AST). We parse complex source code into its AST representation and traverse the tree to identify and isolate semantically coherent, self-contained logical units. The details of the preprocessing pipeline and data sources can be found in Appendix A and Appendix B, respectively.
3.2 DATA CURATION
We aim to build two complementary types of data: text-centric instruction-code pairs (1, C') for tasks like Python data visualization, and vision-centric triplets (1, C, V) for tasks such as chart-to-code.
Guided Evolution. We adapt previously proposed interaction-driven synthesis (Sun al., 2025a) to this strategy, aiming to increase data complexity and diversity. Starting with a seed triplet (L, C) E paired, the evolution is guided by a high-level concept K., represented keywords (e.g., chart type) or a web meta-task ( e.g., 'add a widget'). A new instruction is generated via I' fevolve (I, CC). This conceptual guidance critical for creating grounded and novel instruc- tions for visual coding tasks that move beyond simple heuristic- based evolution (Xu et al., 2024a). Subsequently, the model generates code C' for the new instruction, which then validated in an execution environment F. The feedback from this validation step drives the next synthesis iteration.
Ce-Connexttulizztion. This method enhances the semantic quality of existing paired data, maximiz- ing the utility of our verified code assets. For given pair (I,C) E Dpaired frecontent performs deep analysis of the code to uncover implicit logic, edge cases, or contextual details not specified the original instruction T. It then generates more descriptive and precise instruction, I' frecontexc (T, C). The primary strength of this approach its efficiency: it creates a higher-fidelig pair (T', by improving the quality of the instruction without the computational overhead of synthesizing and validating entirely new code. This ensures the model is trained on a semantically richer dataset where language and code are more tightly aligned.
Reverse Instruction. The primary value of this strategy lies in its ability to transform raw code into aligned instruction-code pairs, thereby substantially expanding data coverage. Inspired by prior practices that exploit large-scale open-source code to synthesize realistic tasks (Wei et al., 2024), we develop a reverse- reverse-instructee instruction process: given a reference file Ciet E Deoder a snippet of K lines Csample is sampled and passed to a function frevare to produce plausible natural language instruction I" = Freverse (Crample) ). A model then generates C" conditioned on I', optionally leveraging Cnr broader context. This pipeline enables the systematic repurposing of theorems and data analysis
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code from scientific PLs like R and Matlab into instruction-folle following samples ( (I', C") effeetively populating our dataset with a rich variety of domain-specific tasks.
Bidirectional Translation. This strategy fosters the learning of abstract, syntax-independen representations by translating conceptual intent between semantically analogous domains (c.g.. Manim and Mathematica), effectively multiplying the value of our specialized datasets. Given sample T C from source domain generated: 1 fransiate (14). Subsequently, the model generates the target code C that uses the source code C as structural template: C firanslate (I C*). This approach pragmatically addresses the challenge of generating complex code from scratch. The process is fully bidirectional.
new instruction for the target domain B is first
After data curation, next component of our toolkit is the validation of synthesized code. We leverage a sandbox E that provides the necessary backends (e.g.. Python interpreters, web renderers). Every newly generated code sample C" must pass through formal execution function, V' Exec (C', EE), to produce a visual output or pass collected / generated test cases. This step ensures that only functionally correct code proceeds the final quality control stage. Samples that fail this validation are rerouted to the synthesis engine for retry and refinement.
3.3
SYNERGIES
Rather than treating data sources in isolation, we deliberately exploit synergies across heterogeneous domains and modalities. The central idea is that knowledge be transferred between semantically related domains (e.g.. R code reinforcing Mathematica tasks) and across different modalities (e.g., the visual output of a Python data visualization task can be used to construct chart-to-code data). This approach is highly effective for mitigating data scarcity in specialized areas, such as scientific demonstration, and enhances the overall coverage and robustness of our dataset.
This principle is applied throughout our data curation process. For instance, the wealth of scientific computing logic R and Matlab corpora generalized to synthesize new data for Manim and Mathematica using our Reverse Instruction and Bidirectional Translation strategies. Similarly, foundational data from WebDev, including HTML and SVG code, provides robust basis for generating complex, interactive scientific demonstrations. This synergy is crucial for broadening task diversity and strengthening model generalization, as we discuss further in Section 6.1.
3.4 DATA QUALITY CONTROT.
While our synthesis pipeline generates substantial executable text-centric and vision-centric code, executability alone is an insufficient proxy for the quality of the generated visual content. It is crucial to recognize that while a program may pass compiler rendering checks, its actual visual output can drastically diverge from user instructions requirements. We therefore construct reward modeling pipeline, tailored to our different data types, to systematically assess and filter out misaligned or low-quality data at scale.
Our reward model employs a VLM as its core engine to assess the quality of data. The reward process, denoted by the function R, takes NL instruction I, the generated code C, and the resulting visual output V. These elements are organized within a structured prompt that guides the VLM through two-stage evaluation: (1) task understanding, where it summarizes its interpretation of the instruction, and (2) multi-dimensional Rating & Scoring across the four key metrics of task relevance, task completion, code quality, and visual clarity.
Fach metric is assigned an integer score on a scale of [1-5]. The final reward score S is calculated as the average of these scores: 5 R(T, C, V). Only data samples whose score S exceeds predefined threshold are retained. For data without a visual output V, similar process is employed using an LLM assess the (1, C¹ pair.
3.5 JANUSCODE-800K
Leveraging data toolkit, construct JANUSCODE- a diverse and high-quality multimodal code intelligence corpus that we will release to the community. To the best of our knowledge, it is the largest and most comprehensive of its kind date. The detailed statisties presented in Table 2.
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Table 2: Statistics of JANUSCODE-800K.
Data Type
Statistics
Text-centric
Python Visualization: Generation
127.5K
Python Visualization: Editing
51.8K
158 #!!
Scientific PLs
31.8K
SVG
20.0K
Visten
Animation
19.5K
(68.1%)
Cho anail
General Artifacts
56.8K
dreserst ceartty D4
Algorithm Data
100.0K
Vision-centric
Chart-to-Code
70.0K
WebUI Generation
200.0K
WebUl Editing
69.5K
Scientific Demonstration
53.0K
Figure 3: Distribution of JANUSCODE-800K.
In terms of its composition, achieve balance between the amount of text-centric and vision-centric data. The overall distribution of task types is shown in Figure 3. During training, JANUSCODERV utilizes the entire corpus, while JANUSCODER is trained exclusively the text-centric data.
DTVBENCH
We present DTVBENCH for evaluating the capability models to generate code for dynamic theorem visualizations. The benchmark integrates two complementary engines: (i) MANIM, an engine for cre- ating explanatory mathematical animations, and (ii) WOLFRAM MATUEMATICA (Wolfram Research, 2025), a symbolic computation engine supporting interactive visualizations. By combining these two domains, DTVBENCH assesses a model's ' ability to translate NL instructions into dynamic, logically coherent, and visually faithful visualizations of theorems.
4.1 DATA COLLECTION AND CURATION
We obtain raw samples from human- authored and verified sources and preprocess them following the method in Section 3.1. Tasks DTVBENCH are derived from code such as 3BLUE BRoWN video segments and official Wolfram demonstrations. From these sources, we manually curated 102 visualization tasks for the benchmark.
4.2 EVALUATION
We adopt multi-dimensional evaluation protocol for both engines. Each generated output is scored along the following dimensions:
Executability (Fexce E {0,, 1})): whether the generated code can be successfully executed.
Code Similarity (8sim E 5]): structural and syntactic consistency with the reference
solution, jolugedn,juugedd by GPT-40.
Instruction A Alignment (Salign E [1,5)): semantic consistency between the natural language instruction and the produced output, judged by GPT-4o.
Faithfulness (Stathh E [1.5]): since dynamic content is primarily intended for human interpretation and interactive outputs are difficult for LLM-based judges to evaluate, we introduce an optional subjective score assessing the plausibility and visual correctness of the generated animation interactive content.
The overall score is defined Sexec (Saim + Salign Staith This ensures that only executable code considered for further evaluation, while successful generations are rewarded for syntactic fidelity, semantic alignment, and perceptual faithfulness. More details of DTV are in Appendix C.

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5
EXPERIMENTS
5.1 EXPERIMENTAL SETTINGS
Data Curation and Synthesis. As described in Section construct complete data toolkit to synthesize training data for multimodal code intelligence. All natural language instructions and code generated using gpt-oss- 1esoos--100b 120b (OpenAI, 2025). For quality control, adopt reward models with (Bai et al., 2025) to evaluate vision-centric data such
different backbones: -rven.55VVL-72B-Imme Python visualizations and rendered webpages, and Qwen3-235B-A22B (Yang et al., 2025a) to handle text-centric data (e.g., Mathematica code).
Backbone Models. For model construction, we use Qwen3-(8B,14B) (Yang et al., 2025a) as the backbones of JANUSCODER, and -reen.55-LL-B--nntttuee (Bai al., 2025) together with InternVL3.5-8B (Wang et al., 2025b) as the backbones JANUSCODERV. In the analysis part, we additionally include Qwen3-4B, Qwen2.5-Code-- -one----oooe----nnssrreee (Hui al., 2024), and LL3.5n for further comparison. Model details are provided in Appendix D.
Baselines. Beyond the backbones used by the JANUSCODER series, we include additional baselines for comparison. For unimodal settings. consider Qwen2.5 5---5--Cor--44BB 14B-Instuuct and Llama-3- 8B (Dubey et al., 2024); for multimodal settings, we adopt MiniCPM-V-2-66 (Yao et al., 2024) and Llama-3.2-11BB Li-----iiio--nssseeeeeee (Meta, 2024). We also report GPT-4o (Hurst et al., 2024) results.
5.2 BENCIMARKING
We thoroughly evaluate the JANUSCODER series by employing broad range of benchmarks that span both unimodal and multimodal code intelligence tasks:
Unimodal Settings. Unimodal benchmarks mainly focus on text-to-code generation, including PandasPlotBench (Galimzyanov al., 2025) for Python visualizations, ArtifactsBench (Zhang et al., 2025) for interactive visual artifacts, and DTV BENCH for dynamic visualization.
Multimodal Settings. Multimodal benchmarks cover ChartMimic (Yang et al., 2025b) for chart-to- code tasks, WebCode2M (Gui et al., 2025) and DesignBench (Xiao al., 2025) for WebUl generation and editing, and InteractSeience (Chen et al., 2025b) for scientific demonstration code generation.
General Coding. We also evaluate on BigCodeBench (Zhuo et al., 2025a) and LiveCodeBench (Jain et al., 2025) to highlight its capability in following complex instructions and algorithmic capability.
5.3
MAIN RESULTS: UNIMODAL TASKS
We first present the results on unimodal tasks in Table 3, where the inputs are mainly NL instructions, code snippets, both. The outputs are code, which are then executed generate figures, animations. or rendered webpages for evaluation.
Table Results on PandasslotBencch. ArtifactsBench, and DTVBENCH.
PandasPlot
DTVBENCH
Model
ArtifactsBench
Incorrect Code. (%)
Visual
Task
Manim Wolfram
Open-Source
LLaMA3-8B-Instuus
36.5
Qwen3-8B
20.0
63
36.5
6.20
5.18
Swen2.5-Coder-711
21.1
63
76
26.0
8.56
4.04
Qwen3-14B
12.6
65
78
39.8
6.63
5.08
Sween2.-Coderr-2B...
35,5
9.61
4.98
JANUSCODER-88
14.9
63
80
39.6
9.70
6.07
JANUSCODER-IPE
9.7
67
86
41.1
8.41
5.97
Proprietary
40
9.7
72
85
37.9
10.60
4.92

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Python Visualizations. We begin by evaluating Python-based visualization tasks (Galimzyanov et al., 2025) where the model generates plotting code from NL descriptions based on DataFrames.
Both 8B and 14B models show strong performance, exceeding baselines with enror rates < 10%, and achieving comparable or superior results to GPT-G T-40 task completion and visual similarity. Moreover, unified models, JANUSCODERV also excels in unimodal tasks, as reported in Table 7.
Visual Artifacts. JANUSCODER delivers results on ArtifactsBench (Zhang et al., 2025) that are sig- nificantly better than GPT-4 4o, which can be attributed our data pipeline that combines challenging webdev data for complex interactive components with aecorem-relatee resources and cross- tross-language code enrich structural diversity and enhance generalization.
Animations and Interactive Contents. On DTVBENCH, JANUSCODER also performs strongly in generating dynamic contents, achieving higher code quality and better subjective evaluations than other basclines, approaching the performance of GPT-40.
5.4 MAIN RESULTS: MULTIMODAL TASKS
We then report the results multimodal tasks in Table 4, where the inputs consist of NL instructions, code, images, or their combinations. The outputs are code, which are subsequently executed or rendered into visualizations interactive pages for evaluation.
Chart-to-Cond Code Tasks. We evaluate JANUSCODERV on ChartMimic (Yang al., 2025b), JANUS- CODERV achieves strong results on both high- and low-level metrics, consistently outperforming baselines and substantially surpassing GPT Go As unified model, it also outperforms recently released specialized chart-to-code MLLMs (Xia et al., 2025; Zhao al., 2025b), highlighting the effectiveness of leveraging cross-lask data synergy. Detailed comparisons are provided in Appendix
Table 4: Results on ChartMimic, DesignBench, WebCode2M, and InteractScience.
ChartMinic
DesignBench
WebCode2M
InteractScience
Model
Customized
Direct
Func.
Visual
Gen.
Edit.
Visual
TreeBLBU
Low
High
Low
High
Overall
CLIP
VLM
Open- Soen-Source
Qwen2.5-VL-7BB.
51.07
58.69
40.73
41.70
72.73
6.85
73.42
12.83
8.40%
45.86
19.83
IntemVL.3-8B
51.88
60.04
48.48
55.41
69.34
7.76
79.62
12.40
8.93%
53.35
22.05
IntemVL33,5-B
51.56
59.55
46.02
53.39
71.73
8.63
79.09
11.95
11.4%
56.79
24.17
MiniCPM-V-2-6
27.53
48.18
21.82
45.26
66.25
4.56
45.85
9.73
0.13%
20.65
7.70
18.87
39.63
19.32
28.37
62.24
6.61
51.54
6.57
6.67%
32.87
13.24
JANUSCODERV-7B
64.72
72.77
65.73
72.73
73.31
8.79
75.78
26.21
17.73%
60.56
27.67
JANUSCODERV-SB
66.68
74.20
65.79
73.18
68.86
8.63
66.34
18.28
17.60%
61.52
33.32
Proprietary
GP 40
59.4
67.42
57.16
64.62
76.83
82.67
13.00
27.20%
70.14
46.01
Webpage Generation and Editing. Models are evaluated on generating or editing HTML code produce webpages grounded in screenshots. In both WebCode2M (Gui et al., 2025) and Design- Bench (Xiao et al., 2025), our models demonstrate significant improvements in both visual quality and the structural similarity of the generated code the references.
Scientific Demonstration Generation. Finally, we evaluate the most challenging and novel task of scientific demonstration code generation (Chen et al., 2025b), which requires the integration of visual understanding, algorithmic reasoning, and spatial comprehension, together with domain knowledge and front-end coding capabilities.
Due to space limitations, the detailed metrics for the results on all the aforementioned benchmarks presented in Appendix E.
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ANALYSIS
6.1
ABLATION STUDIES
Data Synergies. To validate the cross-domain and cross-modal synergies proposed Section 3.3, we conduct ablation studies by selectively removing specific categories of data within JANUSCODE- 800K. The results support our claim, showing that data from non-tare non-target domains, even when cross-modale provide transferable coding capabilities (Sun et al., 2023) specialized visual tasks (e.g,, text-centric data contributing to multimodal coding scenarios). This provides useful guidance for the research community, suggesting that performance in data-scarce scenarios such animations and artifacts can be improved by incorporating data from related, more abundant sources.
Table 5: Ablation studies of JANUSCODER and JANUSCODERV across multiple benchmarks. Results marked with indicate evaluations conducted on subset of the benchmark.
PandusPlodlench
Method
ChurtMimic
InteractScience
WebCode2M
Method
ArtifactsBench
LebV6
Visual
JANUSCODORY
68.74
17.73
75.78
JANUSCODER
80
40.99
w/o Algorithm
70.16"
18.131
72,18
Algorition
621
40.31
17.71
w/o Char12Cade
56.50.J
16.27]
71.92
who SVG
63
821
40.27.
22,86]
12,93U1
71.82,1
who Rewarding
601
774
38.58.
24.57JJ
w/o Rewarding 58.26.4
17.20)
73.78]
Reward Modeling. As shown above, we randomly sample from the synthetic data that passes validation but not filtered by reward modeling. With consistent training set size, we observe a clear performance drop.
This result validates the critical role of our reward modeling for multimodal parts, demonstrating that successful execution alone is insufficient to guarantee high-quality data.
6.2 EFFECT OF BACKBONES
To further validate the effectiveness of our data construction, beyond the original experimental setup we additionally adopt Qwen2.5- 1are225--ooer---55 7B 7B- Ins and Intern VL3.5-4B as backbones. As shown in Figure 4, JANUSCODE-800 consistently yields significant improvements across models with different scales and post-training strategies.
100
25
Raseline
Baseline
JanusCloder
JanusCoderV
PandasPlnt Bonch Taak
90
InteractSclemn OtteractSllenoe-Fui Settteeeeoo--uun Fune-
20
80
15
70
10
60
50
Qwen3-14B
Owen3-88
Qwen2.55 27
Qwen2.5 JntemVLL3.5588 InterVLL-3.5
Figure 4: Effectiveness on different model backbones
This confirms the soundness of our data design and can empower diverse backbones to become more generalist models for multimodal code intelligence. More experiments on different backbones are available in Appendix F.2.
6.3 GENERAL CODING CAPABILITIES
JANUSCODER demonstrates superior general coding capabilities that surpass even specialist ap- proaches. As shown in Figure 5, achieves strong performance on general benchmarks while also outperforming specialist models like VisCoder (Ni et al.. 2025) in their domain. Furthermore, outperforms GPT-4o in both scenarios, which further demonstrates our model's balanced capabilities. More comparisons are provided in Appendix F.I.
target visualization

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Published as a conference paper at ICLR 2026
50
PandasPlateench (Mean)
JanusCader (148)
Artifactrseen (VLM) Judge))
42.5
JanusCoder (149)
GPT-4o
JanusCader 180:
60
Coder (80)
32.5
Owend (8B)
LLaMA3 (ww.A33nsttuutt.00000
Daenn:---oodntaiitttttttt (7B)
62.5
MsCoder (78)
LLaMA33instrettB8
27.5
Qwen2.5 Condl----od--inttuucc (78)
5
10
LiveCodeBench-vG (Pass@1)
25.0
BigCodeBench--ttrd (Pass
20
Figure 5: Visualization balanced visual content generation and general coding ability.
7 CONCLUSION
In this work, we introduce JANUSCODER, suite of foundational models designed to establish unified souuallpogrammatic interface. Supported by a complete and scalable data synthesis toolkit, models handle diverse spectrum of visual code tasks in unified manner Extensive experiments on representative benchmarks, including a new benchmark proposed in this work, demonstrate the stunning performance of the JANUSCODER series, with our 7B to 14B scale models approaching or even exceeding the capabilities of leading commercial models. Further analysis reveals the key principles for building such models. JANUSCODER serves as strong standard for multimodal code intelligence, setting the stage for future advancements in this field.
ACKNOWLEDGEMENT
We thank Fangzhi Xu and Zhangyue Yin for their valuable feedback improving the manuscript's presentation. We also express our gratitude to Zichen Ding for the assistance in addressing challenges encountered during the fine-tuning of LL This research supported by Shanghai Artificial Intelligence Laboratory and WYNG Foundation (HKU 25AG100407), with sincere appreciation for their support.
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Xuanle Zhao, Xianzhen Luo, Qi Shi, Chi Chen, Shuo Wang, Zhiyuan Liu, and Maosong Sun. ChartCoder: Advancing multimodal large language model for chart-to-code generation. In Pro- ceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pp. 7333-7348 Vienna, Austria, July 2025b. Association for Compu- tational Linguistics. ISBN 879-8-89176-22 https://acllattool wtw.logg...rg//..2 wwwp///....... acl- 363/.
doi: 10. 10.18653/v1/22 1201663///200.5.lllooo....333 acl--oo...33 URL
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Pro- ceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Bangkok, Thailand, 2024. Association for Computational Linguistics. URL http: /ttp/aaaiivv//.a///ii//e../aaaaa org/ abs / 240.. 13372.
Terry Yue Zhuo, Vu Minh Chien, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen GONG, James Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, David Lo. Binyuan Hui, Niklas Muennighoff, Daniel Fried, Xiaoning Du, Harm de Vries, and Leandro Von Werra. Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions. In The Thirteenth International Conference on Learning Representations, 2025a. URL https: /pppneevieeeeeeeeeeeeeeeeeeeeeee net. /o///oorumm// lat.um/iiddyyyyy.ll.....
Terry Yue Zhuo Xiaolong Jin, Hange Liu, Juyong Jiang, Tianyang Liu, Chen Gong, Bhupesh Bishnoi, Vaisakhi Mishra, Marek Suppa, Noah Ziems, Saiteja Utpala, Ming Xu, Guangyu Song, Kaixin Li, Yuhan Cao, Bo Liu, Zheng Liu. Sabina Abdurakhmanova. Wenhao Yu, Mengzhao Jia. Jihan Yao, Kenneth Hamilton, Kumar Shridhar, Minh Chien Vu, Dingmin Wang. Jiawei Liu, Zijian Wang. Qian Liu, Binyuan Hui, Meg Risdal, Ahsen Khaliq, Atin Sood, Zhenchang Xing, Wasi Uddin Ahmad, John Grundy, David Lo, Banghua Zhu, Xiaoning Du, Torsten Scholak, and Leandro von Werra. Bigcodearena: Unveiling more reliable human preferences in code generation via execution, 2025b. URL
abs 2510..2500.0 08697.
A DATA TOOLKIT DETAILS
A.1 AST PASRSING
We take the follow steps to process large and complex Manim animations collected from GitHub.
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Table Details about data sources.
Type
Data
# Samples
Sampled?
Original Source
Python Visualization
Viscoder
200,000
(Ni ct al., 2025)
Viscoder
77,000
(Ni et al., 2025)
Char22ooe
Viscodex
210,000
(Jiang 2025)
CodeEvo
70,000
(Sun et al., 2025a)
Algorithm
VisCodex
129,000
(Jiang al., 2025)
1BlucIBrown Video Dataset
68,778
Link
Animation
Kaggle Manim Dataset
414
Link
MMSVG-Icon
10,000
(Yang al., 2025c)
SVG
MMSVG- MMSVG-Illustratio
10,000
(Yang et al., 2025c)
Scientific PLs
TheStackV2
500,000
(Lozhkov al., 2024)
General Artifacts
WebDev Arena
Link
Generation
200,000
(Gui al., 2025)
WehUII
Edit
69,501
(Gui al., 2025)
Scientific Demonstration
Wolfram Demonstrations
Link
AST-based Static Analysis. We employ static analysis approach to process the Manim source files without executing them. Each .py script is parsed into Abstract Syntax Tree (AST), ensuring reproducibility and avoiding environment--peecfic dependencies.
Scene Identification. Within the AST, we detect classes that inherit from canonical Manim hases such Scene and ThreeDScene, For each scene class, locate its construct () method, which encodes the primary animation logic.
Feature Extraction. We traverse the body of the construct () method to extract semantically meaningful features. These include instantiated objects (e.g., Circle, Text), invoked Anima- tions (e.g., Create, Write), and embedded textual content. In addition, we record import state- ments and capture concise code excerpts, while filtering out project-specific dependencies such as manim importss_eee ext.
Data Structuring. The extracted elements are consolidated into structured JSONL entries. Each entry contains the file identifier, scene class, extracted features, and a prompt template. This representation preserves the semantic intent of the animation in format suitable for our data toolkit.
A.2 DETAILS OF GUIDED EVOLUTION
We define a meta task as an abstract, canonicalized edit operation on a web page that captures the essential type of user intent while remaining agnostic the specific context, location, or wording. A meta task therefore denotes operation class, such as "Change the color of a button" or "Add heading text". Each meta task can be instantiated into concrete edit instructions, expressed natural language (e.g.. "Add login button the right side of the navigation bar")) and grounded to specific DOM clements and code edits.
B DATA COLLECTION DETAILS
The sources of data used by our toolkit to build JANUSCODE-SOOG SOOK are presented in Table 6.
C DTVBENCH DETAILS
We construct BENBH by collecting open-source Wolfram demonstrations and Manim scripts, resulting in 52 Manim animation tasks and 50 Wolfram tasks. For the optional subjective evaluation, participants were provided with detailed instructions (attached), and all annotators were college-level students. The benchmark data and testing scripts are included in the supplementary materials.
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Table Complete PandasPlotBench Results.
Incorrect code %
Mean Score
Good (275)
Model
Visual
Task
Visual
Task
Proprictary
GPT-4o
9.7
72
85
0.63
0.85
Open- Weight: LLM
Wwen2.5-Coder--B..
21.1
63
76
0.57
0.75
1ree2.5-Coder-144 14B-Instruct
16.0
63
78
0.62
0.80
LLaMA3-8B-Instuet
26.9
69
0.65
Qwen3-8B
20.0
63
74
0.57
0.76
Qwen3-4B-Ban
17.1
60
73
0.53
0.74
Owen3-8B-Base
17.7
63
75
0.57
0.74
Qwen3- 14B Base
11,4
65
81
0.62
0.82
JANUSCODER-8I
14.9
63
80
0.59
0.8
JANUSCODER-
9.7
67
86
0.57
0.87
Open- Weight: VLM
IB- Vision-Instruct
20.6
77
0.77
InternVL3-8B
20.6
63
73
0.57
0,69
Qwen2.5-VL-72B-Instuu
85
0.7
0.89
Qwen2..-VLL-B-Instuuu
18.3
63
74
0.57
0.73
InterVLL3,5-BB
36,0
52
63
0.43
0.61
JANUSCODERV-7B
18.9
80
JANUSCODERV-BB
26.3
57
72
0.48
0.72
D TRAINING DETAILS
All training experiments are conducted using the LLaMA-Factory framework (Zheng et al., 2024) with 6 precision. Following prior work (Ni et al., 2025) and own observations, adopt a learning rate of x 10 and train for three epochs across all settings. To enable multi-node parallelism and accelcrate training, employ FlashAttention-2 (Dao, 2024), Siger-Kerne (Hsu et al., 2025), and the DeepSpeed framework (Rasley et al., 2020).
For the 4B, 7B, and 8B models, training is performed on NVIDIA H800 GPUs with ZeRO-2 sharding and per-device batch size of 2. For the 14B models, training is carried out on 16 x NVIDIA H800 GPUs with ZeRO-3 sharding and per-device batch size 1. With a gradient accumulation step of 8, the total batch size is fixed at 128 across all configurations.
E DETAILED EXPERIMENTAL RESULTS
E. DETAILED RESULTS ON PANDASPLOTBENCH
We present the complete results PandasPlotBench (Galimzyanov al., 2025) in Table 7.
E.2 DETAILED RESULTS ON CHARTMIMIC
We present the complete results on ChartMimic (Yang et al., 2025b) in Table and Table 9 for direct mimic and customized mimic, respectively.
E.3 DETAILED RESULTS ON DESIGNBENCH
For DesignBench (Xiao al., 2025), Gen. denotes code generation from webpage screenshots and Edit. denotes code modification according to user instructions given screenshots and source codes, highlighting the visual-programmmtti linkage. Table 10 reports the comparative performance of
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Table 8: ChartMimic Complete Results: Direct Mimic
Exec. Rate
Low-Level
High-Lovel
Model
Overall
Text
Layout
Type
Color
Avg.
GPT-4o
Proprietary
VeminiProVision
68.2
52.6
64.2
51.3
47.1
53.8
53.3
53.6
Claude-3-opus
83.3
66.8
83.1
49.9
42.1
60.5
60.1
60.3
GPT-4o
73.0
60.6
67.1
59.0
42.0
57.2
64.6
60.9
Bein-
IDEFICS2-8B
49.0
6.2
33.1
9.2
9.0
14.4
17.6
16.0
DeepSeck-VI--7B
41.3
15.3
26.6
19.7
14.5
19.0
20.4
19.7
LLaVA-Noxt-Yi-34B
50.2
15.9
29.6
17.6
15.2
19.6
20.6
20.1
59.7
14.0
31.1
19.8
17.8
20.7
21.3
21.0
Qwen2-VI-2B
47.0
20.1
29.5
21.3
17.9
22.2
23.4
22.8
50.5
21.3
31.8
18.4
17.0
22.1
24.5
23.3
Inter L2-2B
52.5
23.6
35.8
16.0
15.4
22.7
24.2
23.5
Qwen2-VL-7B VL-7B
67.0
26.4
51.0
31.0
23.3
32,9
35.0
34.0
IntemVL.2-4B
34.7
51.7
25.2
23.6
33.8
38.4
36.1
Intem
61.8
31.5
51.1
28.6
26.2
34.4
38.9
36.6
Min(CPM--lama3--225
80.3
30.7
49.6
38.6
27.6
36.6
42.1
39,4
Phi-3-Vision-128K
66.7
49.6
37.4
29.8
39.8
IntemmV.2-26B
69.3
39.2
58.7
35.9
31.8
47.4
44.4
Qwen2.5VL-7B-Lnstuue
68.1
39.8
58.4
40.2
24.5
40.7
41.7
41.2
IntemVL3.5-8B
66.7
49.2
57.6
44.7
32.6
46.0
53.4
49.7
JANUSCODERV-7B
70.2
75.2
64.5
65.7
72.7
69.2
JANUSCODERV.S.
80.6
70.4
74.2
65.0
53.0
65.8
73.2
69.5
Table 9: ChartMimic Complete Results: Customized Mimic.
Low-Level
High-Level
Model
Overall
Rate
Text
Layout
Type
Color
Avg.
GPT-4o
Proprietary
GeminiProVision
76.2
52.2
70.9
56.0
49.4
57.1
59.6
58.4
Claude-3-opus
88.2
75.2
86.8
54.1
44.3
65.1
65.7
65.4
GPT-4o
73.2
64.1
69.0
60.9
43.5
59.4
67.4
Open- Weight
Owen2-VL-22
35.8
17.4
23,9
19.7
16.5
19.4
21.4
20.4
CogyIm2-llamuu-ccar- 19B
38.7
19.0
27.9
16.5
15.7
19.8
21.6
20.7
ALaVA-Next-Mistrl--2
49.0
20.0
32.0
22.6
19.9
23.6
24.7
24.2
IDEFICS2-8B
49.2
21.6
32.2
18.1
12.2
21.0
27.3
24.2
IntemVLL-22B
49.3
35.4
20.0
18.1
23.9
27.8
LLaVA-Next-Yi-444 Yi-34B
64.2
28.7
44.8
32.9
27.7
33.5
37.1
35.3
DeepSock-VI-7B
59.3
27.5
47.5
36.8
31.5
35.8
39.3
37.6
Phi-3-Vision-128K
67.8
29.7
52.5
42.3
36.5
40.3
44.0
42.1
IntemVL2-4B
74.0
41.3
55.6
39.6
33.1
42.4
47.8
45.1
Qwen2-VL-7B
73.3
41.0
56.3
43.5
34.2
43.8
47.8
45.8
MntemL2--81
73.0
43.1
54.4
39.9
35.4
43.2
48.9
46.1
MiniCPM-Llamaa-V222
78.7
40.8
58.0
44.8
33.2
44.2
51.5
47.9
IntemVL.2-26B
73.7
43.9
62.3
43.5
34.3
46.0
51.1
48.6
Awen2.5VL-7B--15
73.4
$4.9
63.3
$2.0
34.0
51.1
58.7
$4.9
Inter 33B.-88B
71.2
64,9
52.0
34.0
51.6
59.6
JANLSCODERV-7B
80.3
66.4
74.1
66.4
51.9
64.7
72.8
68.7
JANUSCODERV-69
80.7
69.1
75,9
67.8
53,9
66.7
74.2
70.4
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Table 10: Generation and Editing performance across models on DesignBench.
Gen.
Edit.
Model
CLIP
MILM
CMS
Proprietary
Cluub-33--sommee2222555001
81.32
9.15
34.39
Gemini-2.0-Flaate
75.88
9.03
29.05
GPT-40-022411-20*
76.83
9.23
33.94
Open Weight
Qwen2.5-VI--7B-IIs
72.73
6.85
22.33
62.24
6.61
12.99
InterVL3--88
69.34
7.76
26.75
71.73
8.63
28.65
MiniCPM-V-2-6
66.25
4.56
8,89
JanusCoder-7B
73.31
8.79
27.49
JanusCoder-80
68.86
8.63
25.60
proprictary and open weight models on these two tasks, and indicates that the results taken directly from the original paper.
We use CLIP similarity, MLLM Score(MLLM-as-Jude) evaluation. Specifically, CLIP similarity is employed as visual metric to measure the semantic alignment between generated and reference screenshots; MLLM Score is derived by prompting GPT-4o as judge to rate the quality of edits and repairs on a 0-10 scale, which has been validated against human evaluation in the original work; and Code Match Score (CMS) quantifies the overlap of modified lines between generated and ground-trute -truth code using Jaccard similarity.
and CMS (Code Match Scores) for
Among proprietary models, Claude-3.7 (laude-3.7-sonnee 3.7-son achieves the strongest generation capability, while GPT-4o slightly outperforms others on editing with the highest MLLM Score. Both models maintain competitive CMS, indicating robust editing quality.
On the open- ppen-weight side, JanusCode-70 stands out with balanced performance: it ranks first among open-weight models code generation and also delivers strong editing results. Intern VL3.5-8 shows competitive editing ability with the highest CMS, suggesting better alignment for fine-grad fine-grained code modifications. contrast,MiniCPM MiniCPM- V-2-6 exhibit limited code editing performance, reflecting the challenge of scaling down without significant quality loss.
E.4 DETAILED RESULTS ON WEBCODE2M
The detailed WebCode2M (Gui et al., 2025) results are presented in Table 11, for metrics:
Visual evaluates whether the generated webpage resembles the reference in appearance at the image level.
TreeBLEUU assesses whether the generated code preserves the structural correctness of the webpage at the DOM tree level.
TreeBLEU measures the fraction of all 1-height subtrees in candidate tree that be matched in reference tree. Formally, let S(.) denote the set of 1-height subtrees; then TrecBLEU is given by
TreeBLEU S(() S(())
|S())
where and represent the candidate and reference trees, respectively.
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Table 11: Short/Mid/Long performance across metrics on WebCode2M. For proprictary models, the specific model versions are not publicly disclosed in the original paper.
Short
Mid
Long
Model
Visual
TreeBLEU
Visual
TreeBLEU
Visual
TreeBLEU
Proprietary
Gemini
0.35
0.16
0.38
0.15
0.34
0.14
Claude
0.52
0.13
0.35
0.14
0.37
0.13
GPT-4V
0.68
0.12
0.65
0.11
0.62
0.10
GPT-4o
0.85
0.15
0.81
0.13
0.82
0.11
Open- Weight
Qwen2.5-VL-7B-Im
0.72
0.14
0.76
0.13
0.72
0.11
L
0.53
0.08
0.56
0.46
0.05
IntemVIL3-8BB
0.80
0.14
0.80
0.13
0.79
0.11
IntemVVL3.5-2
0.81
0.13
0.80
0.12
0.77
0.11
MiniCPM-V-2-6
0.47
0.11
0.45
0.10
0.45
0.09
JanusCoder-77B
0.79
0.25
0.75
0.73
0.26
JanusCoder-8B
0.69
0.20
0.69
0.19
0.60
0.16
As shown in Table 11, proprictary models generally achieve stronger visual alignment, with GPT-4o leading across all lengths. However, TreeBLEU scores reveal different trend: while proprietary models perform competitively in appearance-level fidelity, their structural correctness remains limited.
Among ween-w models, JanusCoder-7B and JanusCoder-88 achieve significantly higher Tree- BLEU scores, surpassing all proprietary counterparts and setting the state-of-the in structural preservation of generated code. This indicates that JanusCoder excels capturing the DOM-level organization of webpages, which is critical for generating code that is both usable and extensible. Although JanusCoder's visual similarity is slightly lower than the best proprictary models, the results demonstrate a favorable trade-off: JanusCoder prioritizes structural faithfulness without severely sacrificing appearance quality.
Overall, these findings highlight JanusCoder as the first open-weight model that narrows the gap with proprietary systems visual fidelity while establishing new benchmarks for structural correctness on WebCode2M.
E.5 DETAILED RESULTS ON INTERACTSCIENCE
INTERACTSCIENCE is benchmark designed to evaluate the capability of LLMMs in the generation of scientific demonstration code. The benchmark includes two complementary components. The Programmatic Functional Test (PFT) measures functional pass rate of generated code, reported with three metrics: Overall (fraction of all test cases passed), Average (mean accuracy across samples), and Perfect (percentage of cases where all tests for one sample are passed). The Visually- Grounded Qualitative Test (VQT) assesses semantic alignment between generated outputs and visual demonstrations, The Action score reflects whether the intended interaction sequence correctly executed. CLIP similarity and VI.M-uulge scores capture automated and model-based evaluation of visual grounding quality, respectively.
As shown in Table 12, proprietary models such as Gemini-2.5-Pro achieve strong performance, especially in perfect pass rate of PFT and VLM-judge quality of VQT. Open-weight baselines, however, lag behind, with most models struggling functional correctness and visual alignment. By contrast, our JanusCoder models (JANUSCODERV-7) and JANUSCODERV -8B) substantially improve existing open- open-weight systems. They outperform strong alternatives such Intern Intern VL3.5 and Llama-3.2. 2-113 IB across nearly all metrics, achieving higher programmatic correctness PFT and more consistent alignment in VQT (e.g., +5-10 points VL.M-udge).
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Table 12: Programmatic Functional Test (PFT) and Visually-Grounded Qualitative Test (VQT) results.
PFT
VQT
Model
Overall %
Average
Perfect %
Action %
CLIIP
VI.M-Judge
Proprietary
GPT-40
31.07
28.59
10.49
88.47 86.44
71.18
46.01
Gemini-2.5-PP
41.87
38.56
13.99
72.66
55.26
Open-Weight Weight
Qwen2.5-VL--7B-nnste
8.40
7.05
0.70
67.29
45.86
19.83
8.93
8.13
1,40
74.24
53.35
22.05
1ntemVVL3.58B
11.47
10.92
2.10
80.34
56.79
24.17
MiniCPM-V-2. 6-2.6
0.13
0.08
0.00
29.66
20.65
7.70
Llama-3.2-11B
6.67
5.63
0.70
46.44
32.87
13.24
JANUSCODERV-77B
17.73
16.91
4.20
83.22
60.56
27.67
JANUSCODERV-SB
17.60
17.30
4.20
81.86
61.52
33.32
Table 13: Evaluation across sub-domains on ArtifactsBench.
Model
AVG
GAME
SVG
WEB
SI
MS
Qwen3-8B
36.52
34.58
36.37
38.08
36.15
35.92
JANUSCODER-8B
39.60
36.39
30.47
40.07
41.92
44.75
Qwen3-14B
39.79
38.65
39.50
41.22
38.68
38.67
JANUSCODER-14B
41.10
39.54
24.72
44.47
41.49
45.04
GPT-40
37.97
36.96
39.54
39.27
35.73
35.83
E.6 DETAILED RESULTS ARTIFACTSBENCH
ArtifactsBench (Zhang et al., 2025) benchmark designed evaluate large language models on program and artifact generation tasks across different domains. The benchmark covers multiple sub-tasks, including GAME (Game development), SVG (SVG Generation). WEB (Web Application), SI (Simulation), and MS (Management System). Each sub-task reflects specific application scenario, testing the model's 's ability to generate domain-relevant, functional, and executable artifacts.
As shown in Table 13, JanusCoder demonstrates competitive performance compared with other models. The 14B variant of JanusCoder achieves the highest average score (41.10), outperforming both Qwen3 and GPT-4o. Notably, JanusCoder- 14B achieves the best results WEB (44.47), SI (41.49), and MS (45.04), indicating its strong capability in handling practical system and application- level generation tasks. Although its performance SVG Generation is relatively lower, the overall results highlight the superior adaptability and effectiveness of JanusCoder in diverse artifact generation domains.
F DETAILED ANALYSIS AND COMPARISONS
F.1
GENERAL CODING CAPABILITIES
More experiments on balancing visualization capability and general coding capabilities are in Figure 6.
F.2 EXPERIMENTS ON DIFFERENT BACKBONES
More experiments the effectiveness of our method regarding different model architectures and sizes are shown Figure We can see that our method can vastly improve the performance of various models across different benchmarks.
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F.3 MODSS ADJUDICATION
To mitigate potential evaluation circularity and bias from relying on single judge backbone in our reward modeling pipeline, conducted cross-model adjudication study, We randomly sampled 30K unfiltered data samples (covering Python and WebUT artifacts) and evaluated them using three distinct frontier models: Owwn2.5-VL--22-Immme
Gemini- Prroo, and GPT-5.
Table 14: Cross-Model Adjudication
Qwen2.5-VL (Mean)
Qweaa2.-VL. (Var)
Gemini2,5-Pee (Mean)
Gemini2,5-Pro (Vur)
GPT-5 (Mean)
GPT3 (Nar)
Krippendotff's.
Python Artifacts
0.76
4,28
0.47
4.03
0.61
4.16
0,52
0.71
The evaluation yielded Krippendorff's a above 0.70 across both domains, indicating strong inter- annotator agreement among different model families. This confirms that reward scores robust, objective, and generalizable indicators of data quality.
F.4 HUMAN EVALUATION ON SUBJECTIVE TASKS
To further validate the subjective visual quality of our generated artifacts, which often difficult to capture fully via automated metrics, we conducted a human evaluation subset of 100 generated samples (comprising Manim animations and InteractScience WebUls), Three annotators with college- level backgrounds evaluated the outputs on 1-5 scale based on visual fidelity and instruction alignment.
Table 15: Human Evaluation on Subjective Tasks
Model
Manim Code2Video
Model
InteractScience WebUI
Qwen3-8b
2.1
Qwen- Dwen-2.5-VL-77b
0.92
Qwen3-14b
2.64
InternVL-3..0 VL-3.5-4B
1.67
Qwen-2.5-coder-7hh
2.48
VL-3r5VV.33
1.57
Qwwn-2.5-coder-32b
2.82
CPT-4o
2.78
JANUSCODER--148
3.14
JANUSCODERV-7B
3.19
As shown in Table 15, the JANUSCODER series consistently outperforms the baseline models, correlating strongly with our automated VLM-based evaluations and confirming its superior ability to generate perceptually high-quality visual content.
G CASE STUDIES
We present some studies of generated UIs and artifacts, shown in Figure Figure and Figure 10.
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H PROMPTS
The prompt examples we used in JANUSCODER are listed below.
Synthesis Prompt- Viscode Generation
You will be given two example descriptions of data visualization. Your task is to generate a new visualization instruction.
Here your generation logic:
1. If the given description is about data visualization (charts, plots, maps), create instruction that can visualize similar problem or make different kind of plot;
2. If the given description is NOT about data visualization, create a brand new visualization instruction based on the core topic of the original description.
Your output should have two part: plot description and plot style description, and you should follow the following format:
1. Plot Description: Your new plot description
2. Plot Style Description: Your new description for plotting style
The example descriptions are: Example [Instruction inserts here]
Example 2:
[Instruction 2 inserts here]
Reward Prompt- Viscode Generation
You will be given a triplet of information:
1. A natural language Inst ruct iuction;
2. The Code generated to fulfill it;
3. The resulting Image.
Your evaluation must follow a detailed Chain of Thought process, analyzing each component before assigning score.
EVALUATION FRAMEWORK
STAGE COMPREHENSIIE TASK UNDERSTANDING
Deconstruct the user's request to identify all explicit requirements (e.g., chart type, title, colors) and implicit intents (e.g.. the information be conveyed).
Analyze the Instruction:
Analyze the Code: Review generated code for correctness, logic, and quality.
Analyze the Image: Inspect the final visual output assess its accuracy and clarity.
STAGE 2: MUL.T--IMMENIONAL RATING & SCORING
Based your analysis, you will rate the triplet four dimensions. Then, you will provide final score based on the detailed guidelines below.
EVALUATION DIMENSIONS
1. Task Completion: This measures the extent to which the final image and code successfully fulfill all aspects of the instructed task.
Does the image accurately represent the data and adhere to all specified chart types, labels, and titles?
Accuracy:
Completeness: Are all parts of the instruction addressed?? Are any requirements missing?
2. Solution Coherence & Code Quality:
Logic & Efficiency: Does the code follow a logical and efficient sequence of operations to generate the visualization?
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Correctness & Readability: Is the code syntactically correct and executable? Does it follow standard programming best practices for clarity?
3. Visual Clarity: This assesses the aesthetic and communicative quality the final image.
Readability: Is the chart easy to read and interpret? Are fonts, colors, and labels clear?
Aestheties & Layout: Is the visualization well-designed and visually appealing? Is the layout balanced, free of clutter and overlapping elements?
4. Task Relevance: This measures the practical, real-world value assigned task.
Practicality: Does the instruction represent realistic and useful data visualization scenario?
Value: Does the task
meaningful benchmark for valuable Al capability?
SCORING GUIDELINES (1-5 SCALE)
(Excellent): task is perfectly completed with no flaws. The code efficient, clean, logical. The visual output is clear, accurate, and aesthetically excellent. A flawless submission.
(Good); The task is mostly completed and achieves the objective, but with minor, critical issues. This could be small element missing from the chart, slight code inefficiency, minor visual imperfections.
(Fair): The task only partially completed, the output has significant flaws. For example, chart type is wrong, the data misrepresented, the code highly inefficient, or the visual cluttered and hard read.
(Poor): The solution attempts the task but deviates significantly from the instructions. The code may run, the resulting image largely incorrect, misleading, irrelevant to the user's request.
The task fails completely. The code is non-executable, produces error, or the output is completely unusable.
(Failed):
OUTPUT SPECIFICATION
Your final output must be single JSON object. must include your detailed Chain of Thought reasoning, a score for each the four dimensions, and final Total Score (the average of the dimensional scores).
ILLUSTRATIVE EXAMPLE
Data Triplet:
Instruction: "Generate a horizontal bar chart showing projected 2024 revenue 'Prod- uct Alpha'. 'Product Beta', and 'Product Gamma'. Revenues are $4M. $5.5M, and $3.2M respectively. Use a blue color palette and title the chart 'Projected Revenue 2024'."
Code:
import matplotlib. pyplot plt
products = ['Product Alpha' 'Product Beta', 'Product Gamma!
revenues = (4, 5.5, 3.2]
plt. flt. (figaize= (10, 6))
plt. (producta, revences, color='skybie skyblue") plt.xlabel ('Projected Revenue (in Millions) pit.ylabel ('Product')
# Note: The title was forgotten in the code. aat.tight_Layout plt.showe
Image: [An image horizontal bar chart with the correct data, labels, and blue color.
However, the chart has no title.]
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Output:
"Chain of Thought": "1. **Task Understanding:** \ The instruction requires a horizental bar chart for three products \ with specific revenue figures. It explicitly asks for blue A palette and specific title, 'projected Revenue 2024'.
2. *.Code Analysis:**
The Python code uses matplotlib correctly. It defines the correct \ data and uses "barh" for horizonta chart. The color 'skyblue' \ fits the 'blue color palette' requirement. However, the 1ine add the title ('plt.ti:le(...)) executable.
missing, The code is clean and
3. **Image Analysis:***
The image shows the correct chart type and data. The axes are labeled correctly. The color is blue. The only mi missing element is the title specified in the instruction.
4. *<Rating:**
Task Completion is flawed because the title is missing. Solution is logical, just incomplete. Visual A Clarity good but could better with title. Task Relevance high as this is a very common business chart.", "Task Completion"; "3", "Solution Coherence \ Code Quality": "4" "Visual Clarity": "4", "Task Relevance": "5", "Total Score"; "4.0"
Coherence is good the
The resulting image is given at beginning. The natural language instruction is: [Instruction inserts here] The code generated is: [Code inserts here]
Reward Prompt- Viscode Edit
You will be given triplet of information:
1. The Initial Image (before the edit).
2. The Edit Instruction natural language command).
3. The Edited Image (the result after applying the instruction).
Your evaluation must follow strict, threc-step process determine final binary outcome.
EVALUATION FRAMEWORK
STEP 1: COMPREHENSIVE ANALYSIS
Analyze the Initial Image Instruction: First, understand the content of the Initial Image and deconstruct the Edit Instruction ion to identify the user's core intent. What object needs to be changed, added, or removed? What style attribute needs to be modified?
Analyze the Edited Image: Carefully compare the tdited Image with Init ial Inage. Identify all changes that were made and assess their fidelity to the instruction.
STEP DIMENSIONAL SCORING (INTERNAL THOUGITT PROCESS)
As part of your reasoning, you will mentally score the edit across three critical dimensions on 1-5 scale. This scoring is part of your thought process to reach the final judgment.
EVALUATION DIMENSIONS (1-5 SCALE)
1. Instruction Adherence: How well did the edit follow the user's command?
5 (Perfect): The instruction was followed perfectly, including all nuances.
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4 (Good): The main goal of the instruction was achieved, but with minor deviations (e.g.. "make the car red" results in slightly orange car).
3 (Fair): The instruction was only partially followed (e.g.. "remove the two people" only removes one).
2 (Poor): The edit attempts the instruction but fundamentally misunderstands or fails to exccute it.
(Failed): The edit completely ignores or acts contrary to the instruction.
2. Edit Quality & Realism: high is technical and artistic quality edited portion?
5 (Excellent); The edit is seamless, photorealistic, and indistinguishable from real photograph. No artifacts.
4 (Good): The edit is high quality but has very minor, barely noticeable artifacts imperfections.
3 (Fair): The edit is noticeable. There are visible artifacts, unnatural textures, or slight inconsistencies lightin//shadows.
2 (Poor): The edit of low quality, looking obviously fake "pasted on." Contains significant, distracting artifacts.
(Failed): The edited is chaotic of pixels, completely broken, nonsensical.
3. Preservation of Unrelated Areas: How well were the parts of the image not meant to be edited preserved?
5 (Excellent):
Only the targeted area was modified. The rest of the image is completely untouched and pristine.
(Good): The edit mostly contained itself to the target area but caused tiny, insignificant changes elsewhere.
3 (Fair): The edit bled into other areas, causing noticeable but not catastrophic changes to the background or other objects.
2 (Poor): The edit significantly damaged or altered other important parts of the image.
(Failed): The entire image is distorted or corrupted as a result of the edit.
STEP 3: FINAL JUDGMENT (FAIL/SUCCESS)
Based on your dimensional scores, you will make a final binary judgment. Rule: The final result is (Success) if and only if all three dimensional scores are 3 or higher. If any dimension scores or 2, the final result must be 0 (Fail). This ensures that any significant flaw constitutes failure.
OUTPUT SPECIFICATION
Your final output must be single JSON object containing your detailed Chain of Thought and the final binary Final Result. You should also include your internal scores for transparency.
ILLUSTRATIVE EXAMPLE:
Initial Image: [A photo of brown dog sitting green grass next white fence.]
Edit Instruction: "Change the grass to snow."
Edited Image: [The grass is white, but the dog's paws blurry and partially erased, and patch of snow incorrectly covers part white fence.]
Output:
"Chain of Thought"; "1. **anayysis:* The user wants replace the
'green grass' with 'snow' while keeping the dog and fence intact.
2. **Dimensional Scoring:**
a) **Instruction Adherence:** The grass was indeed changed to snow, so the main instruction was followed, Score: 4.
b) *-Edit Quality \a Realism:**
The edit on the dog's paws poor, with noticeable blurring and erasure. This makes the edit look fake. Score: 2.
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c) **Preservation of Unrelated Areas:**
The edit incorrectly spilled onto the white fence, altering an object that should have been preserved. Score: 2.
3. **Final Judgment:**
since two dimensions scored below 3, the edit failure.", "Instruction Adherence Score": 4,
"Edit Quality \4 Realism Score"; 2, "Freservation of Unrelated Areas Score"; 2, "Final Result";
The Initial image and Edited image given at the beginning. Edit Instruction [The edit instruction inserts here]
Generation Prompt- Artifacts Query
You are an HTML, JavaScript, and CSS expert. Please use your professional knowledge to generate accurate and professional responses. Generate HTML code to meet the following requirements. Make sure the code you generate is executable for demonstration purposes. Query: [Query inserts here]
Generation Prompt- Artifacts Plan
You expert in frontend web development (HTML, JavaScript, CSS). Your task is generate complete HTML document containing necessary interactions animations based on the following HTML implementation plan. When generating the complete HTML file, you must strictly follow component list, element types, and ID definitions provided in the plan, while ensuring that the overall structure, layout, and interaction logic are consistent with it. You may use HTML, CSS, and JavaScript, and if any component requires external libraries such Plotly, Chart.js, MathJax, they should be included via CDN. The final HTML file must be fully standalone, directly runnable web browser. Query: [Query inserts here]
Synthesis Prompt- Artifacts Rewrite
are an expert in prompt rewriting for code generation tasks. Your task rewrite a user query into an instruction prompt that clearly asks generating website, web page, or HTMI HTMI JavaScript interface implementing the described idea.
Each rewritten prompt must:
You can reasonably expand the original intention, but don't deviate from the original intention of designing website or web page.
Explicitly mention that the task is to build website, webpage, HTML, HTML- JavaSeiit implementation (e.g., "You generate accurate and professional responses. Make sure the generated code is executable for demonstration. Please use HTML and JavaScript to implement a character leveling up and skill tree system.") "). Don't 't copy the example, be as diverse possible.
code expert. Please your professional knowledge
Follow the structure and expressive style shown in example, but avoid directly copying
Use clear wording suitable for code generation.
Produce three rewritten version per input query, ensuring diversity in phrasing and structure.
Avoid repetition: do not use the same sentence structure or format more than once.
Avoid rigid templates overly predictable patterns such as "Make website that..." "Create a page for..."
Input Format: Query: [Example original user query inserts herc]
Ouiput Format:
1. [[xample rewritten query example inserts here]
[Example rewrilten user query example inserts here]
3. [Example rewritten user query example inserts here]
4. |Example rewritten user query example inserts here]
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5. [Example rewritten user query example inserts here]
Query: [Target query inserts here]
Reward Prompt- Artifacts
You are a Senior AI Data Visualization Synthesis Quality Assurance Expert. Your mission is to provide rigorous, objective, and multi- faceted evaluation of AI- Al-generated data visualizations. You will he given triplet of data: natural language Instruct fulfill and the resulting rendered maag (a screenshot).
the Code (HTML/CSS/IS) generated to
Your evaluation must follow a detailed Chain of Thought process, analyzing each component before assigning score.
EVALUATION FRAMEWORK
STAGE 1: COMPREHENSIVE TASK UNDERSTANDING
Deconstruct the user's request to identify explicit requirements (c.g., chart type, title, colors) and implicit intents (c.g., the information to be conveyed).
Analyze the Instruction:
Analyze the Code: Review the generated HTMLJCSS/IS code for correctness, logic, and quality.
Analyze the Image: Inspect the final rendered screenshot to assess its accuracy and clarity.
STAGE MULTI-DIMENSIONAL RATING & SCORING
Based on your analysis. you will rate the triplet across four dimensions. Then, you will provide a final score based on the detailed guidelines below.
EVALUATION DIMENSIONS
1. Task Completion: Measures extent to which final image and code successfully fulfill all aspects of the instructed task.
Accuracy: Does the screenshot accurately represent the data and adhere to all specified chart types, labels, and titles?
Completeness: Are all parts of the instruction addressed? Are any requirements missing?
2. Solution Coherence & Code Quality:
Logic & Efficiency: Does the code follow a logical and efficient sequence of operations to generate the visualization? Is the HTML structure semantic? Is CSS/JS used effectively?
Correctness Readability: Is the code syntactically correct and renderable in a browser? Does it follow standard web development best practices?
3. Visual Clarity: Assesses the aesthetic and communicative quality of the final screenshot.
Readability: the chart easy to read and interpret? Are fonts, colors, and labels clear? Aesthetics Layout: the visualization well-designed, balanced, and free clutter?
4. Task Relevance: Mcasures the practical, real-world value of the assigned task.
Practicality: Does the instruction represent realistic and useful data visualization scenario?
Value: Does the task
meaningful benchmark for valuable AI capability?
SCORING GUIDELINES (1-5 SCALE)
(Excellent): Task perfectly completed with no flaws. Code is efficient, clean, and logical. Visual output is clear, accurate, and aesthetically excellent.
(Good): Task is mostly completed with minor, con-critical issues (e.g., missing small element, slight inefficiency, or minor visual imperfections).
(Fair): Task is partially completed, with significant flaws (c.g., wrong chart type, misrepre- sented data, cluttered visual).
(Poor): Task deviates significantly from instructions. Code may render, but screenshot largely incorrect or irrelevant.
(Failed);: Task fails completely. Code is non-renderable or output is unusable.
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OUTPUT SPECIFICATIONS
Your final output must be single JSON object. include your detailed Chain of Thought reasoning, a score for cach the four dimensions, and final Total Score (the average of the dimensional scores).
ILLUSTRATIVE EXAMPLE
Data Triplet:
Instruction: "Generate a horizontal bar chart showing the projected 2024 revenue uct Alpha'. 'Product Beta', and Produuct Gamma'. Revenues are $4M. $5.5M, and $3.2M respectively. Use a blue color palette and title the chart 'Projected Revenue 2024"
Code:
DDOCTYPEE html>
<html>
<head>
<script arc="httpa://ddn
</head>
<body>
<canvas id="nyChart"
<script>
const ctx towument.get.leneee ('myChart .getConteet ('2d");
new Chart (ctx,
type: 'bar',
data:
labels: ['Product Alpha', 'Produst Beta', 'Product Gamma ']
datasets: II
label: 'Revenue (in Millions)', data: [4, 5.5, 3.2],
backgroundColor: (54, 162, 235, 0.6) borderColor: '5g,a(54, 162, 235, 1),, borderMidth:
}1
options: {
indexAxis: 'y', // Makes the bar chart horizental
plugins:
// Note:
The title configuration forgetten in the code.
legend: {
display: false
}
));
</script>
</ </body> </html>
Image: A screenshot of the rendered HTML page containing a horizontal bar chart with correct data, labels, and blue color. The chart has no title.
Output:
"Chain of Thought"; "1. **Task Understanding:*x \ The instruction requi res a horizental bar chart for three products \ with specific revenue figures. It explicitly asks for blue palette and specific title, 'Projected Revenue 2024".
2. **Code Analysis:**
The HTML code uses the Chart js library to correctly generate the visualization. It defines the correct data and uses "indexAxis: 'y' create horizontal chart. The "backgroundColor" fits the 'blue
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color palette' requi rement. However, the "options.ppuu" wwt.s..puginn.iiii.. con figuutiiion block is missing, so the speci fied title is not rendered. The code is well-structured and renderable. 3. *vImage Analysis:*:
\
The screenshot shows the correct chart type and cata. The axes are \ labeled correctly. The color is blue. The only missing element is the chart title specified in the instruction.
4. **Rtiin::**
flawed because the title is missing. Solution Coherence & Code Quality is good as the code is logical and uses \ standard library, but is incomplete. Visual Clarity is good but could be better with title. Task Relevance is high as this is very common business chart.",
Task Completion
"Task Completion": "3",
"Solution Coherence & Code Quality": "4", "visual Clarity": "4", "rask Relevance"; "5",
"Total Score"; "4.0"
Synthesis Prompt- Wehpage Edit Instructions Generation
You are expert HTML/CSS developer.
You will receive screenshot of web page.
Your task to generate concrete edit instructions for the web page that bring visually noticcable changes to the page. An edit instruction is composed of an edit action, a visible UI element, and an edit attribute.
EDIT ACTION TYPES
1. Add (introducing new UI elements)
2. Change (modifying elements)
3. Delete (removing elements)
EDITABLE UI ELEMENTS
1. Button (clickable element for user actions, e.g., "Submit", "Save")
2. Input field (form element for text or data entry, such as textboxes or number inputs)
3. Card (container element for grouping related content, often with border shadow)
4. List item (individual entry within a list, such as menu or todo items)
5. Divider (horizontal or vertical line used separate content sections)
6. Heading (text element indicating section titles, e.g., <h1>, <h2>) and links)
7. Navigation bar (top-level
8. Image (pictures, logos, or illustrations)
9. Icon (symbolic graphic, e.g., checkmark, star)
10. Table (rows and columns of data)
EDITABLE ATTRIBUTE TYPES
1. text (including content, font, and typography modifications)
2. color (encompassing background colors, text colors, and accent colors)
3. position (spatial arrangement and layout adjustments)
4. size (dimensional scaling and resizing operations)
5. shape (geometric modifications and structural changes)
6. layout & spacing (holistic modifications affecting entire UI components)
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REQUIREMENTS FOR GENERATING EDIT INSTRUCTIONS
1. Visual Impact
Every instruction must produce a clear, visually noticeable change (e.g., layout restructuring, color scheme shifts, adding or removing visible components).
2. Visual References Only
Always describe target elements by their appearance or position on the page (e.g.. "the large green button at the bottom right", "the navigation bar at the top"). Never use code-specific terms like class names, IDs, or HTMI. tags.
3. High-Level Intentions
Express edits as general intentions rather than precise technical details (c.g., say "move the button closer to the edge" instead of "move the button by 10px").
4. Interactivity
Exclude interactive behaviors such as hover states, animations, or JavaScript-hased actions.
5. Screenshot- GereenshotGGroundd Grounded Only
not mention information that could only known from inspecting HTML/CSS source. Rely solely on what is visible in the screenshot.
6. Element Relationships Multi-Property Changes instruction must either:
Involve at least two elements in relation to each other (e.g., alignment, grouping, ordering, spacing).
Combine multiple changes single element into one instruction (e.g., "make the card smaller and add gray border").
7. No Redundancy
Avoid overly similar repetitive instructions (e.g.. do not output both "Swap the first and second buttons" and "Swap the third and fourth buttons").
8. Output Format
Generate 3 to 5 instructions as numbered list, with explanations or extra comments. If no suitable instruction be generated, output exactly word: "None".
EXAMPLE INSTRUCTIONS
[Example instruction inserts here]
OUTPUT INSTRUCTIONS
Synthesis Prompt- Wehpage Edit
You are an expert HTML/CSS developer.
You take piece of code a reference web page, and an instruction from the user.
You need to modify the code according to the user's instruction to make the wehpage satisfy user's demands.
Requirements:
Do not modify any part of the web page other than the parts covered by the instructions.
For images, use placeholder images from httss:/placcholld.o
Do not add comments in the code such as "<!- Add other navigation links as needed ->" and "<!- ... other news items ->" in place of writing the full code. WRITE THE FULL CODE.
You MUST wrap your entire code output inside the following markdown \ fences: "html and
Do not output any extra information or comments.
INSTRUCTION:
[Instruction inserts here]
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CODE:
"hhmml
[Code inserts here)
OUTPUT:
Reward Prompt- Webpage
are Senior Quality Assurance Expert Al-Generated HTML/CSS Code Editing and Visual- ization.
Your mission is to provide a rigorous, objective, and multi-faccted evaluation of Al-generated code modification tasks.
You will be given:
1. the original rendered Image (the first input image),
2. the modified rendered Image (the second input image),
3. the natural language Instruction (user's command for modification),
Your evaluation must follow detailed Chain of Thought process, analyzing each component before assigning score.
EVALUATION FRAMEWORK
STAGE 1: COMPREHENSIVE TASK UNDERSTANDING
Break down the user's request into explicit requirements (c.g.. "change hackground to blue", "add a red button", "remove the chart title") and implicit require- ments (e.g., style consistency, element positioning).
Analyze the Instruction:
Compare Images: Identify what has changed between the original and modified image. List all observed modifications.
Match Against Instruction: Verify whether the observed image modifications directly and fully correspond instruction. Check if there are missing elements, extra unintended changes, or partial compliance.
STAGE MULTT-DIMENSIONAL RATING SCORING
Based on your analysis, you will rate the given example across five dimensions, Then, you will provide a final score based on the detailed guidelines below.
EVALUATION DIMENSIONS
1. Instruction Fultillment
Accuracy: Does the modified code and its rendered image correctly implement every requested change?
Completeness: Are all aspects of the instruction covered without omissions?
2. Modification Precision
Unintended Changes: Were there any modifications not requested by the instruction?
Minimal Necessary Change: Was the change scope minimized to only what was required, avoiding collateral edits?
3. Modification Recall
Faithfulness: Did the modification preserve all unrelated elements from the original code and image?
Was any original information, layout, visual element inadvertently lost, degraded, or corrupted?
No Content Loss:
4. Visual Quality & Consistency
Clarity: Is modified element clear, readable, and well-rendered?
Consistency: Does the change blend naturally with the rest of the image layout break, no visual artifacts)?
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5. Task Relevance & Usefulness
Practicality: Does the instruction represent realistic and useful web-editing scenario?
Value: this example a good henchmark for evaluating code-editing and web UT understanding capabilities?
SCORING GUIDELINES (1-5 SCALE)
(Excellent): All instructions perfectly implemented; extra changes; code and visuals are clean and consistent, code quality is high.
(Good); Instruction mostl implemented with only minor imperfections or negligible extra changes. Code and visuals are generally high quality.
(Fair): Some parts of the instruction are missing incorrectly applied: noticeable issues in code, visuals, unintended changes.
(Poor): Major deviation from instruction: significant missing or wrong modifications; poor code or visual quality.
(Failed): Instruction not followed at all, or modifications are irrelevan//ncooreet;; code may be broken non-renderable.
OUTPUT SPECIFICATIONS
Your final output must be single JSON object. It must include your detailed Chain of Thought reasoning, a score for each the five dimensions, and final Total Score.
The Total Score should reflect your holistic, overall judgment of the result as a whole, not simple arithmetic average of the five dimension scores.
If you give score of 5, you you give a score below 5, you must list which requirements are violated.
explicitly state that all requirements perfectly satisfied. If
All scores for each criterion must be integers (1, 2, 3, 4, or 5). Do not assign fractional or decimal scores to any item, including the overall score.
ILLUSTRATIVE EXAMPLE
Input Data:
Original Image: [A screenshot of the original HTML page. In the real input, it is the first image.]
Modified Image: [A screenshot of modified HTML page. In real input, the second
image.]
Instruction: Position the address box alongside the sidebar menu and adjust the text color inside the box match the main text color for consistency.
Output:
"Chain Thought"; "The instruction requires positicning the address box alongside the sicebar menu and ensuring its text color \ matches the main text. In the original page, the address box is \ centered above the content, not aligned with the sidebar. the modified version, visually next to the sidebar menu. The text color remains black, matching the main content. The implementation uses absclute \ positioning, achieving two-column layout but with some alignment \ and responsiveness limitations. No unrelated elements are changed. The result visually fulfills the instruction with minor technical and aesthetic compromises.", "Instruction Fulsillment": 4,
address box appears
top right,
"Modi fi cictioon Precision"; 5, "Modification Recall": 5,
"Visual Quality & Consistency"; 4,
"Task Relevance & Usefulness"; 5,
"Total Score":
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INSTRUCTION
[Instruction inserts here]
OUTPUT
Generation Prompt- Webpage Generation
You are an expert HTML/CSS developer. You take screenshots a reference web page from the user, then build single page apps using HTML/CSS.
Make the app looks exactly like the screenshot.
Pay close attention to background color, text color, font size, font family, padding, margin, border, ctc. Match the colors and sizes exactly.
Use the exact text from the screenshot.
Do not add comments in the code such as "<!- Add other navigation links as needed ->" and "<!-... other news items ->" in place of writing the full code. WRITE THE FULL CODE.
Repeat elements as needed to match the screenshot. For example, if there are items, the code should have 15 items. DO NOT LEAVE comments like "<!- Repeat for cach news item ->" or bad things will happen.
For images, use placeholder images from https://placcholl..co co include a detailed description of the image in the alt text so that an image generation Al can generate the image later.
Please return the code within the markdown code bl ock "*hhtml and at the start and end.
Do not output any extra information or comments.
The screenshot: <image>
Generation Prompt- Webpage Edit
You are an expert HTML/CSS developer. You take a screenshot, piece of code of reference web page, and an instruction from the user. You need to modify the code according to the user's instruction to make the webpage satisfy user's 's demands.
Requirements:
Do not modify any part of the weh page other than the parts covered by the instructions.
For images, use placeholder images from https://placehold.coo
Do not add comments in the code such as "<!- Add other navigation links as needed ->" and "c!-... other news items ->" in place of writing the full code. WRITE THE FULL CODE.
You MUST wrap your entire code output inside the follewing markdown \ fences: '*html and
Do not output any extra information or comments.
Instruction: [Instruction inserts here]
Code: [Code inserts here]
The wehpage screenshot: <image>
Synthesis Prompt-
You exceptionally intelligent coding assistant and creative problem generator for the language. take small piece code as inspiration and build complete, high-quality programming problem and a runnable solution around it.
You will be given a "seed snippet" of R code. This snippet is for inspiration only.
Seed Snippet:
[Code spippet inserts here]
Your Task:
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1. Get Inspired: Look at the functions, packages, or logic in the seed snippet (e.g., subset. dplyr: filter, ggplot).
2. Create New Problem: Invent completely new, realistic, self-contained programming problem that a user might have. This problem should be inspired by the seed but not be about explaining or fixing the seed itself.
3. Write Full Solution:
runnable code solution for the problem you just invented. The solution must be self-contained. If it requires package, it should include library () If it needs data, it must create sample data frame.
Provide complete, high- high-quality,
Output Format: You MUST present your output in exactly distinct sections: [Problem Description] and [Code Solution].
Synthesis Prompt- Mathematica
are an expert coding assistant and creative problem generator the Wolfram I Language (Mathe- matica). You take small piece of code as inspiration and build a complete, high-quality programming problem and runnable solution around it.
You will be given "seed snippet" of Wolfram Language code. This snippet is for inspiration only.
Seed Snippet:
[Code snippet inserts here]
Your Task:
1. Get Inspired: Identify the core concept in the seed (e.g., DSolve, Plot, Manipulate),
2. Create Problem: Invent a completely new, self-contained problem. For example, the seed is DSolve (elveee... ...... 1, you could create a problem about solving a different type of differential equation or visualizing its solution field.
3. Write Full Solution: Provide complete, runnable Wolfram Language solution. must self-contained and clearly solve the problem you invented. Use standard conventions and add comments (+
*) for clarity.
Output Format: You MUST present your output in exactly distinct sections: [Problem Description] and [Code solution].
Synthesis Prompt- Matlab
You are a highly skilled coding assistant and a creative problem generator for MATLAB. You take a small piece of code as inspiration and build complete, high-quality programming problem and runnable solution around it.
You will be given "seed snippet" of MATLAB code. This snippet for inspiration only.
Seed Snippet:
[Code sni spip inserts here]
Your Task:
1. Get Inspired: Observe the functions operations in the seed (e.g.. matrix multiplication, plot, signal processing functions).
2. Create a New Problem: Invent a new, self-contained engineering or scientific problem. For instance, if the seed is about matrix multiplication, you could create problem about solving a system of linear equations applying transformation.
3. Write Full Solution: Provide a complete, runnable MATLAB script or function. It must be well-commented (3) and self-contained. If it generates plot, ensure it is fully labeled.
Output Format: You MUST present your output in exactly two distinct sections: [Problem Description] and [Code Solution).
Synthesis Prompt- Manim
You are an expert Manim designer tasked with enhancing existing animations. Based on the script below, write new, more advanced instruction. Your new instruction must include all the original animation's
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features AND add at least one significant new feature. Examples of new features could be: animating a related mathematical formula, adding explanatory text, transforming the existing shapes into new ones, introducing more complex sequence of animations. Describe the complete, enhanced animation in single, detailed paragraph.
Here the original Manim script:
"python
[original codes inserts here]
Benchmark Prompt- DTVBenchh Manim
ROLE
You expert Manim developer and strict JSON-only grader.
TASK
Evaluate the [GENERATED CODE] against (REFERENCE CODE) and (INSTRUCTICN) using the rubric below. Then OUTPUT ONE SINGLE-LINE JSON OBJECT ONLY.
RUBRIC (TWO DIMENSIONS, SCORES ARE INTEGERS 1..5)
1. Code Similarity: how close the implementation logic/structure/APl usage is the reference.
2. Instruction Alignment: well final animation (sequence/contee//iiinn)) matches the instruction.
INPUTS
INSTRUCTION] [Instruction inserts here]
RREFERENCE CODE]
[Reference code inserts here]
[GENERATED CODE]
[Generated code inserts here]
OUTPUT SCHEMA (MUST MATCH KEYS AND TYPES EXACTLY)
Return ONE minified JSON object with EXACTLY these keys:
"ode side__emmllarty "score": <int 1-5>, "reasoning"; "<<=60 words, no newline>"
}, iinstructioo__lllgmmeet
"score": <int 1-5>, "reasoning": "<<-60 words, newl inex
HARD CONSTRAINTS READ CAREFULLY
Output ISON ONLY. No markdown, code fences, prose, prefix/suffix.
NOT wrap with "** or "jsoon
The FIRST character MUST be (( and the LAST character MUST be }..
Single line only (no newline characters). trailing commas.
Use integers 1..5 for "score".
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Human Evaluation Guidelines DTT Manim
HUMAN EVALUATION INSTRUCTIONS
You will be shown:
1. Instruction natural language description of the animation.
2. Generated Video animation produced by a model hased on this instruction.
Your task is judge how well the Generated Video matches the Instruction. Please provide from 1 (Very Poor) (Excellent), and optionally add short comment explain your decision.
SCORING GUIDELINE
(Excellent): The video completely follows the instruction, including all details (objects, text, colors, positions, animation effects, sequence, timing, etc.).
(Good): The video follows the instruction very well, with only one or two small mistakes (e.g.. slightly wrong color or layout).
(Acceptable): The video captures the main idea of the instruction but misses several details or gets one major aspect wrong.
(Poor): The video only partially follows the instruction; many important parts are missing or incorrect.
(Very Poor):
The vidco does not follow the instruction all; looks unrelated.
OUTPUT FORMAT
For each video, please provide:
Instruction Alignment Score (1-5):
Comments (optional); A brief note on why you chose this score.
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42.5
JanusCoda (148)
05-
40.0
Januxtader
60-
JanusCeder (881
37.5
Ower3 (88)
PandasPlotBenchh
35.0
75-
Owen3 1881
70-
VoCoder (78)
LLaMA3 instruct (98)
Owen2.5 Iodeentttuut (78)
65
15
26
25
30
BigCodeBench- Hard Complete (Pass@1)
BagCoddeench-HarrdcCooo (Pass@ 1]
50
42.5
(146)
65-
40.0
JanusCoder (88)
80-
MuusCoder (B))
37.5
Oweed
PandasPlatBench
ArtfactssBenc
Quen
VipCader (7))
Coder
69
0
75
Biggoodeeench-Harddnnsnst (Pass@1)
10.0
125
17.5
20.0
22.5
BigCodeBenchh-Hard Instuut (Pass@1)
12.5
15.0
37.
22.5
25.0
45.0
PandasPlo:Bench (Mean)
JanusCoder (148)
42.5
JanusCoder (14B
GPT
JanusCodor (8B)
JanusCoder (8B)
Artfactstennh (VLM
GPT-4o
LLaMA3-inssuctt (8B)
Qwen22 C-nnen---Codr--sttuuttttc (7B)
VisCader (78)
LLaMA3-Instrurt (88)
Qwen2.5 Instrut
65
LiveCodeBench-v5 (Pass@11)
20
25
30
LiveCodeBenct-V5 (Pass
30
45.0
PandasPlod PoodssslotBBehhh (Mean)
JanusCoder (148)
ArtifactsBench (VLM judgo)
42.5
JanusCader (14B)
GPT-4o
40.0
Januscoder
JanusCoder
LLaMA3-Irstruct (8B)
Qwen2.5, Cww.S..ooorrnnttruccte
75
VisCoder (7B)
LaMMa-instruct (BB)
85
10
LiveCodeGench-ve (Pass@1)
1s
20
25
30
LiveCodeBench-v6 (Pass@
Figure 6: Plot-related performance versus general coding capabilities of different models (all results)
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Baseline
Baselina
JanusCoder
JanusCoder
PondasPlolBencte -aaieenc---akkk Taask
Artifacts (1āte)
50
40
60
20-
Qwenn3-1 4B
Qwen3 8B
Qwen2.58 55 Coder
Qwen3-11B
Qwen3-8B
Qwen2.5- 5-Coder-7B
Baseline JanusCoderV
Baseline
JanusCoderV
ChartMinic (Direct)
70
StterceSSrenne Pune
20-
15
10
40
5
Qwen2.5.VL.n VL IntermVL 8. IntemVL.3.5 2.L.353..44 4B
InternVL.3.5-83 Qwen2.5-VL-7B 5-VL-7B
InteruVL-3.5-1B
Figure 7: Effectiveness of our method on various model backbones (all results)
This demo visualises how different climate-poiccy parameters affect the evolution of atmospheric carbon dioxide and annual emissions. the curves update real
Gobal
Gemini- P.PPPo
JanusCoder-B
References
Multiple Steady States in Continuously Stirred Tank Reactor
Geminl 2.5-Poo
JanusCoder-SB
References
Figure Generated artifacts in InteractScience
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Published as a conference paper at ICLR 2026
WebEditing Color
Qwvan255VL.7
WebEditing Style
Efforlesss Code Depioyments
Effortiess
Code Deployments
Qwwn-25-VL.78
JanusCuderV:77
Gold Refecence
Figure 9: Generated UIs in DesingBench
Develop a plot using polar projection within rectangular grid. The plot map the 'Teta' values against values DataFrame, This will require transforming the angular data from degrees radians and setting polar coordinate system in rectangular plot Labels added specific both angular radial
Qwen3-8B
JanusCoder-5B
Figure 10: Generated figures in PandasPlorBench
40


Reviews:
[{"review_id": "NO7hNoPYRD", "reviewer": "Reviewer_or79", "summary": "This paper presents JanusCoder, a suite of multimodal models aiming to unify code generation and visual reasoning through a “visual–programmatic interface.” The authors introduce a large-scale data synthesis toolkit and release JanusCode-800K, a multimodal corpus covering charts, WebUIs, visual artifacts, and animations. Two models—JanusCoder (text-centric) and JanusCoderV (vision-centric)—are trained on this corpus using Qwen and InternVL backbones. The authors also propose DTVBench, a benchmark for dynamic theorem visualization tasks. Experiments across seven benchmarks show that JanusCoder models outperform open-source baselines and sometimes rival GPT-4o. Ablation studies indicate that cross-domain synergies and reward-based data filtering contribute significantly to performance.", "questions": "- What exactly differentiates the “unified visual-programmatic interface” from standard multimodal fine-tuning?\n- Could you quantify the impact of reward modeling (e.g., pre- vs. post-filtering data quality metrics)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Ambitious and timely goal: a unified model bridging code logic and visual semantics.\n\n- The JanusCode-800K dataset appears large, diverse, and potentially impactful for future research.\n\n- Strong empirical results on both text- and vision-centric benchmarks, including new ones created by the authors.", "weaknesses": "- Model novelty is limited. The architecture largely reuses Qwen and InternVL; the “unified interface” claim feels more conceptual than technical.\n\n- Weak quantitative evidence for data quality improvements. The reward model and filtering pipeline are described but not systematically validated.\n\n- Overextended scope. The paper attempts to be both a dataset, benchmark, and model paper, which dilutes its main scientific contribution.\n\n- Lack of human evaluation for subjective visual tasks (animations, WebUIs).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "n65MbnoYcz", "reviewer": "Reviewer_NYgb", "summary": "JanusCoder expands code intelligence from purely text-based inputs to jointly reasoning over code and visual outputs. It introduces a scalable multimodal code-data synthesis pipeline and builds JANUSCODE-800K, the largest multimodal code corpus to date. The authors develop unified models that handle text-centric and vision-centric coding tasks, achieving performance comparable to or surpassing commercial systems while offering insights into aligning program logic with visual expression.", "questions": "JANUSCODE-800K is central to the contribution. Will the full dataset be publicly released at camera-ready?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces a unified visual-programmatic interface and a complete multimodal code data synthesis toolkit, enabling code models to reason jointly over textual and visual programming tasks.\n\n2. The work builds JANUSCODE-800K, the largest multimodal code corpus, and conducts extensive experiments across diverse benchmarks and modalities, demonstrating careful design and thorough empirical evaluation.\n\n3. Results show strong performance, with 7B, 14B models approaching or surpassing commercial systems across text-centric and vision-centric code tasks.", "weaknesses": "1. Unclear data release plan: The data synthesis pipeline and JANUSCODE-800K corpus are central contributions, yet public availability is not guaranteed at submission time, raising concerns about reproducibility and community impact. The paper should explicitly clarify the dataset release schedule and scope.\n\n2. Judge-based evaluation bias: The Stage-3 refinement heavily depends on LLM/VLM judges, which risks evaluation circularity and bias toward the same models used for filtering. More human evaluation or cross-model adjudication would strengthen reliability and reduce bias.\n\n3. Limited methodological novelty: Despite strong engineering effort, the paper reads largely as a dataset and data-pipeline contribution. The model setup closely follows existing architectures and training paradigms, making the work feel more like a large-scale data release than a method advance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HXaElRxBY0", "reviewer": "Reviewer_uHBB", "summary": "This paper introduces JanusCoder, a family of multimodal code-generation models that integrate textual, visual, and programmatic information. Its key contributions include:\n1.  JanusCode-800K Dataset: A new dataset covering both text-centric and vision-centric tasks;\n2.  DTVBench Benchmark: A new benchmark for dynamic theorem visualization using Manim and Mathematica;\n3.  Strong Performance: Comprehensive evaluations on eight benchmarks show JanusCoder performs on par with or surpasses GPT-4o and specialized open-source models.", "questions": "See the Weakness part.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1.  This paper introduces a comprehensive dataset (JanusCode-800K), which fills a clear gap by integrating visual, textual, and programmatic modalities at scale;\n2. The paper is well-written, with clear motivation and structured sections to detail the data curation process;\n3. JanusCoder obtains strong empirical results across >8 benchmarks;", "weaknesses": "1. DTVBench’s limited scale (~102 tasks) may constrain statistical reliability;\n2. Why does JANUSCODERV-8B perform worse than InternVL3.5-8B on DesignBench and WebCode2M?\n3. When considering visual information, besides benchmarks purely focused on visualization, there are also some algorithmic or reasoning-related benchmarks [1]. Has the paper evaluated or discussed model performance on such tasks?\n4. A question mark appears at line 253.\n\n\n[1] MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)