VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models
Authors: Lingjie Jiang
Shaohan Huang
Xun Wu
Yixia Li
Guanhua Chen
Dongdong Zhang
Furu Wei
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language Models
Code Generation
Model Merging
Dataset Construction
Multimodal Code Generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Multimodal large language models (MLLMs) have significantly advanced the integration of visual and textual understanding. However, their ability to generate code from multimodal inputs remains limited. In this work, we introduce VisCodex, a unified framework that seamlessly merges vision and coding language models to empower MLLMs with strong multimodal code generation abilities. Leveraging a task vector-based model merging technique, we integrate a state-of-the-art coding LLM into a strong vision-language backbone, while preserving both visual comprehension and advanced coding skills.

To support training and evaluation, we introduce the Multimodal Coding Dataset (MCD), a large-scale and diverse collection of 598k samples, including high-quality HTML code, chart image-code pairs, image-augmented StackOverflow QA, and algorithmic problems. Furthermore, we propose InfiBench-V, a novel and challenging benchmark specifically designed to assess models on visually-rich, real-world programming questions that demand a nuanced understanding of both textual and visual contexts.

Extensive experiments show that VisCodex achieves state-of-the-art performance among open-source MLLMs and approaches proprietary models like GPT-4o, highlighting the effectiveness of our model merging strategy and new datasets.

Published as a conference paper at ICLR 2026
VISCODEX: UNIFIED MULTIMODAL CODE
GENERATION VIA MODEL MERGING
Lingjie Jiang
Shaohan Huang! 1x
Xun Wu!
Yixia Lit.3 Guanhua Chen
Dongdong Zhang'
Furu Wei
Microsoft Research
Peking University
"Southern University of Science and Technology
pku. edu.edd {shachanh, fuwei femicisstttoocccccoomm
ABSTRACT
Multimodal large language models (MLLMs) have significantly advanced the inte- gration of visual and textual understanding. However, their ability to generate code from multimodal inputs remains limited. In this work, we introduce VisCodex, a unified framework that seamlessly merges vision and coding language models to empower MILMS with strong multimodal code generation abilities. Leveraging a task vector- based model merging technique, we integrate a state-of-the-art cod- ing LLM into a strong vision-language backbone, while preserving both visual comprchension and advanced coding skills. To support training and evaluation, we introduce the Multimodal Coding Dataset (McD), a large-scale and diverse collection of 598k samples, including high-quality HTML code, chart image-code pairs, image-augmented StackOverflow QA, and algorithmic problems. Further- more, propose InfiBench-V, a novel and challenging benchmark specifically designed to assess models on visually-rich, real-world programming questions that demand a nuanced understanding of both textual and visual contexts. Extensive experiments show that VisCodex achieves state-of-the-we performance among open-source MI.LMs and approaches proprietary models like GPT-4o, highlighting the effectiveness of our model merging strategy and new datasets. Our code and data are available at https: /tip::u//gthub.... com/ www.///aainnggiiiioaa ieeiiiiinnneeeiiooeeeee
INTRODUCTION
Multimodal large language models (MLLMs) have achieved remarkable success recent years, demonstrating an impressive ability to understand and reason about the world by integrating informa- tion from both visual and textual domains (Zhu et al. 2023 Liu et al. 2023 Bai et al. 2023). These models have pushed the boundaries of what is possible in tasks like visual question answering (VQA), image captioning, and general multimodal conversation. However, a critical and highly practical domain remains relatively underexplored: the generation of functional code from visual inputs.
This task, which we term multimodal code generation, presents a distinct set of challenges. It demands not only a nuanced interpretation of visual such as UI layouts, data chart structures, or programming-relatee sereenshots- also the ability to translate these insights into syntactically flawless and functionally correct code. While today's multimodal models excel visual description, they often lack the deep programming knowledge required for robust code generation. This gap is critical, as many modern development tasks, like translating UI mockup into HTML or replicating a data chart, demand seamless fusion of visual understanding and coding proficiency.
To bridge the gap between visual perception and code generation, we introduce VisCodex. Rather than relying on costly pre-training, our approach efficiently creates a unified model by arithmetically merging the parameters of a state-of-the-are vision-language model and a dedicated coding LLM. Specifically, we adopt model merging technique based on task vectors, which capture the parameter shifts resulting from fine-tuning on specific domains (c.g., lisiou-language, coding). By lincarly combining these task vectors in the language model backbone- and cross-modal projection modules intact-we jointly integrate advanced code understanding and generation capabilities with nuanced visual perception. This enables the resulting model to simulta-
keeping the vision encoder
"Corresponding Author

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Published as a conference paper at ICLR 2026
neously retain strong visual understanding and robust code generation ability, thereby significantly enhancing its performance on multimodal coding tasks. Our experiments show that the merged model significantly outperforms the original vision-language model on multimodal coding tasks.
To address the lack of high- high-quality, large-scale training data for multimodal code generation, We intro- duce the Multimodal coding Dataset (McD), comprehensive, instruction-tunigg dataset comprising 598k samples. McD is meticulously curated from four diverse sources: (1) aesthetically enhanced and structurally sound HTML code generated from webpage screenshots, (2) high-quality chart-to- code pairs from real-world and synthetic sources, (3) image-augmented question-answer pairs from StackOverflow, and (4) foundational algorithmic coding problems preserve core reasoning abilities.
Furthermore, to rigorously assess the real-world performance of models on multimodal coding QA task, develop InfiBench-V, a new and challenging benchmark. InfiBench-V consists of visually rich gegramming-relatee questions derived from real user scenarios where the images are indispensable for arriving at the correct solution. provides a more realistic and demanding testbed than existing benchmarks that often focus either text-only code QA or simpler visual tasks.
Our primary contributions are threefold:
1. We propose VisCodex, novel approach for creating powerful multimodal code generators by merging vision and coding models, demonstrating new and efficient path to capability enhancement.
2. We introduce McD, a large-scale, high-quality dataset for cneruuction-tunnngg MLLMs wide spectrum of multimodal coding tasks, and InfiBench-V V, a challenging benchmark for realistic evaluation. We will release both the dataset and benchmark to facilitate reproducibility and future research.
3. We conduct extensive experiments showing that VisCodex significantly outperforms existing open-source MLLMs and achieves performance competitive with leading proprietary models like GPT-4o, thereby setting state of the art for open-source multimodal code generation.
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Figure Illsstattion of the VisCodex pipeline. (a) Model merging strategy for unifying vision- language and coding LLMs; (b) Data distribution and representative cases of McD; (c) Category breakdown and representative cases of InfiBench-V.
VISCODEX
2. MODEL ARCHITECTURE
typical multimodal large language model (MLLM) is comprised of three primary components: a vision encoder, language model backbone, and projection module connect the modalities (Li
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al. 2024a). The vision encoder's role is to extract visual features from input images. These features are then projected by the projector module into the language embedding space. Subsequently, the language model integrates these visual representations with textual inputs, enabling multimodal understanding and reasoning.
Many existing MLLMs, such earlier versions of Qwen- VL Bai al. 2023) and LLaVA Li et al. (2024a), are limited by fixed image input resolutions, which curtails their flexibility in processing images of varying sizes (Bai al. 2003 Liu al. 2023). To overcome this limitation, Qwen2.5- VL Wang et al. 2024); Bai et al. 2025) introduces a 2D Rotary Position Embedding (RoPE) (Su
al. 2024) mechanism within its Vision Transformer (ViT) Dosovitskiy et (2021). p. This allows for the flexible processing of images with arbitrary resolutions by dynamically generating visual
tokens. This approach preserves the absolute scale and spatial relationships of objects within the
image. Given its enhanced flexibility and performance, we adopt the Qwen2.5- VI. architecture as our foundation model.
2.2
MODEL MERGING
To enhance the coding capabilities of our multimodal large language model without undertaking costly retraining from scratch, we employ model merging (Jin et al. 2022). By arithmetically combining the parameters of specialized models, can integrate distinct skills and create unified, more versatile model without requiring access to the original training data (see Figure (a) for an overview of the model merging pipeline).
Task Vectors. Central to model merging are task vectors (llharco et al. 2022). which quantify parameter shifts resulting from fine-tuning a base model on specific task. Given a pretrained base model Ousee and its task-specific fine-tuned variant On, task vector is formally defined as:
Frask
On
Obase
(1)
Such vectors encapsulate the parameter changes necessary for a model specialize particular domain or capability and serve as modular, transferable units of knowledge across models and tasks.
Multimodal and Code Capabilities. Our goal enhance the multimodal large language model by incorporating advanced code understanding and generation capabilities. Considering that code-related expertise predominantly resides in the language model backbone, we restrict our merging process to this component. We retain the original visual encoder and cross-modal projection modules unchanged to preserve the intrinsic visual understanding capabilities of the MILM.
Specifically, define the task vector for the language model component of the Vision-Languuge Model (VLM) as:
Tvlm
Ovim
Opase
where Tvlm captures the parameter shift that enables the language model to effectively handle multi- modal inputs by jointly processing visual and textual information.
Analogously, define the task vector for the coding model, encapsulating its capability for code comprehension and generation:
Teode
Ocode
Ohase
Merging Strategy. Following the linear merging method of Thharoo et al. (2022) and its application to enhancing multimodal mathematical reasoning in Chen et al. (2025). we adopt a similar strategy to transfer code reasoning abilities into MLLM. The updated language model parameters, combining both multimodal and code-related knowledge, are computed as follows:
OvisCodex Obase ATvim + (1 A)
where the hyperparameter [o, 1] controls the trade-off between retaining original multimodal representations and integrating new code expertise. OvieCadex is the initialization of the parameters of VisCodex.
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Implementation Details. Our model merging process targets only the language backbone of the VLM, leaving the vision encoder and cross-modes -odal projection modules unaltered. This selective merging approach allows for clear attribution of performance gains while significantly reducing computational overhead. To construct our primary code task vector (Teode), we select a coding model that shares the same architectural foundation as the VL.M's language backhone. Since Qwen2.5-VI language model is derived from Qwen2.5, we utilize o wwa-ee-eeeoooon-------- al. 2025). For our 33B scale model, we correspondingly use the OpenCodeReessoingg-Nenn -emmorrnn- 1.1-32B (Ahmad et al. 2025) variant. Furthermore, in our ablation studies, we create and evaluate code task vectors from two other prominent code- snde-specialized models, Qwen2.5 -- InItrrutttt (Hui) al. 2024) and Open Opennhiinee22775 (Guha et al. 2025), to verify the effectiveness of merging with
(Ahmad)
code-specialized task vector, as shown in Table
2.3
MODEL TRAINING
After model merging, we perform supervised fine-tuning on our Multimodal Coding Dataset (McD), further aligning the merged model with multimodal coding tasks. To efficiently leverage both the pretrained visual grounding and newly integrated code abilities, we freeze the vision encoder and projection modules, ane-tuning only the language model backbone.
McD DATASET
We introduce the Multimodal Coding Dataset (McD), new large-scale dataset designed for instruction-uuinng multimodal models on coding tasks. McD is constructed from four primary components, each targeting distinct aspect of multimodal code understanding and generation:
1. Enhanced HTML Code: We generate aesthetically and structurally improved HTML code by redesigning and augmenting existing webpages.
Chart Image-Code Pairs: We construct high-quality chart-code pairs by sourcing Python mat- plotlib code from GitHub, which are then refined through a multi-stage filtering and rewriting process.
3. image-Augmented Code QA: We extract real-world,
question and answer pairs from StackOverflow and subject them to rigorous cleaning and refinement.
4. Algorithmic Code: We aggregate and curate data from established algorithmic coding datasets to preserve and enhance the model's core reasoning and mooblem-solving abilities.
The data distribution and representative cases for the four domains are illustrated in Figure (b), and
more comprehensive statistics are available in the Appendix E.1 The following sections describe each component detail.
3.1 ENHIANCED HTML CODE
A review of the existing Web2Code dataset Yun et al. 2024 revealed several shortcomings, including broken image links, rudimentary CSS, and visually unappealing designs. Our initial approach to address these issues involved using GPT-4o to directly rewrite the existing HTML code. However, this method proved suboptimal, as the constraints the original code structure frequently led to rendering artifacts and visually incongruous layouts.
To overcome this, we adopted a novel, image-driven generation pipeline. We first curated 560,000 webpage images from Web2Code serve stylistic seeds. GPT-4o then prompted to design entirely new webpages inspired by these seeds. The resulting HTML was rendered using Play- wright to capture screenshots. A rigorous filtering pipeline was then applied discard rendering failures, images with anomalous dimensions, and other visual artifacts. This process yielded 200,000 high-quality, newly generated code-image pairs. These pairs were subsequently converted into an instruction-followwi format using the framework provided by Web2Code.
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3.2 CHART IMAGE TO CODE DATA
To build a diverse and high-quality chart dataset, we incorporate both synthetic and real-world data sources. For synthetic data, we include the 164,000 synthetic Chart2Code samples released by ChartCoder (Zhao et al. (2025b) as part of our training data. For real-world data, we curated 46,000 chart-code pairs from GitHub.
Inspired by data engineering strategies (Ding et al. 2023 Chiang et al. 2023 Xu et al. 2023), we first collected 129, 99,000 real-world Python matplotlib scripts from GitHub. This raw data, however, suffered from significant quality issues, including non-executable code, inconsistent formatting, and potentially harmful snippets. To mitigate these issues, we employed GPT-4o to systematically rewrite and normalize the code, while simultaneously classifying each script by chart type. Subsequently, multi-stage, rule-based filtering pipeline was applied to eliminate low-quality samples. This pipeline removed scripts that failed to exccute, produced blank or improperly sized images, generated visually corrupted outputs (e.g., heavily pixelated charts). As a final quality assurance step, we leveraged GPT-4o to score the aesthetic and functional quality of the generated charts, retaining the top 46,000 high-quality image-code pairs.
The final dataset combines the 164,000 synthetic samples with 46,000 curated real-world ex- amples, resulting in a comprehensive collection of 210,000 chart image-code pairs for instruction tuning.
3.3 IMAGE-AUGHO AUGMENTED CODE QA
StackOverflow represents rich repository of real-world. code-centric QA data, particularly valuable when augmented with illustrative images. Our collection process involved crawling StackOverflow for QA threads containing images, followed by an initial filtering step to retain only those with an accepted answer containing either Python HTML code.
rigorous data cleaning pipeline was implemented ensure quality, removing entries with exces- sively short verbose answers, invalid URLs, broken image links, and blank oversized images. We also identified that many accepted answers were suboptimal for training, being either too terse for clarity overly verbose. To address this, utilized GPT-4o to refine these answers by removing sensitive content, rewriting unclear sections, and enhancing overall conciseness and clarity. This multi-stage pipeline yielded final dataset of 59,000 high-quality, image-augmented StackOverflow QA pairs suitable for instruction tuning.
3.4 ALGORITHMIC CODE
To maintain the model's proficiency in algorithmic reasoning and code generation, we incorporate algorithm-related code data from Kodcode (Xu et al. 2025). Specifically, select samples from five
categories: LeetCode (Hartford 2023), Codeforces (urlcek] 2022). TACO (Li et al., 2023). Code Contests (Li al. 2022), , Algorithm (The Algorthmss] 2023 Keon 2018). The final collection
contains 129,000 algorithm-related cestruction-oolooviin examples.
INFIBENCH-V
We introduce InfiBench-V. new benchmark designed evaluate the ability of multimodal large language models to answer complex programming questions that integrate both text and images. While existing benchmarks like InfiBench (Li et al., (024c) focus on text-based code QA, InfiBench- V is specifically constructed to assess multimodal reasoning, where visual context is critical to formulating a correct answer.
4.1
DATA CURATION
Our benchmark is built upon a rigorous, multi-stage curation pipeline using data from Stack Overflow. The process began by scraping initial set of approximately million image-based questions that included a community-vent veriffie "accepted answer" to ensure solution quality. We then narrowed this pool 40,000 recent and high-engagement questions. The most critical refinement step involved
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using GPT-4o to isolate samples where the image is indispensable, filtering questions solvable by text alone. This yielded a core set of 10,000 high-relevance, multimodal questions.
We categorized these samples based programming domain and, guided by the class distribution and sampling principles of InfiBench, domain experts manually selected 322 questions to form the final benchmark. These span 13 programming languages, each mapped to of five high-level categories: front-end, back-end, data science & machine learning (DS&ML), mobile and desktop development, and IT operations (ITOps). The detailed category breakdown and representative cases are shown in Figure (c)
To ensure quality and prevent model memorization in pre-training, implemented a prompt paraphrasing process. Domain experts rewrote each question in a concise and directive manner while preserving its semantic content. Each question is also annotated with its evaluation category and associated metrics, including set of key phrases and reference answer to support robust scoring.
4.2 EVALUATION CRITERIA
To objectively assess the quality of answers across diverse range of question types, we adopt three-pronged evaluation strategy inspired by InfiBench. For each benchmark question, domain experts select one or more evaluation methods, and the final score for that question is obtained by averaging the normalized results of the selected methods.
Keyword Matching. We observed that for majority of questions, answer quality is closely tied to the presence of specific keywords. Our domain experts craft a set of rules for each question, specifying essential terms and phrases. To capture nuanced requirements, these rules can be simple checks, regular expressions, or complex logical statements. When multiple keywords are required, they can be individually weighted to ensure that the most critical components of the answer contribute more significantly to the final score.
Unit Testing, For questions where the answer is primarily block of code, we verify its correctness using unit tests. To facilitate automated evaluation, domain experts supplement the question with precise requirements, like function names and expected I/O formats. They also provide the necessary setup and teardown scripts, creating complete and executable environment for programmatic validation.
GPT-4o Judge. For questions that rely heavily on natural language understanding, we leverage GPT-4o score MLLM responses by comparing them with the accepted reference answer. The evaluation considers both answer correctness and completeness across two dedicated scoring dimensions.
EXPERIMENTAL SETUP
Evaluated Benchmarks. We evaluate our model four multimodal benchmarks assess range of multimodal- coding skills:
Design2Code (Si et al. 2024): This benchmark measures the ability to translate visual UI designs into executable code. We report the average performance on both Low-Level (Low-L) features (Block, Text, Position, Color) and High-Level (High-L) semantic fidelity.
ChartMimic (Shi et al., 2024): This benchmark evaluates the generation of chart specifications from images. We adopt the Direct Mimic task on the test-mini subset and report both Level (Low-LL) and GPT-4o-assessed High-Level (High-I.) scores.
MMCode (Li et al. 2024b): This benchmark assesses algorithmic problem-sole solving in visually rich contexts. Performance is measured by pass @ accuracy (Chen et al.) [2021).
InfiBench-V (Ours): For our proposed benchmark, we report the average score across all defined evaluation metrics.
Training Settings. our main experiments with the 8B model, which uses the code task vector from aaanCoodeRessonnnnneetttte Nemotronn-.1177B we determined the optimal merge coefficient À by evaluating performance on the MMCode benchmark. From a set of candidate values {0.7, 0.8, 0.85, 0.9}, we selected À 0.7. According to merging formula this applies weight of 0.7 to the vision-

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language task vector (Telm) and 0.3 to the code task vector (Toode). Detailed training hyperparameters and training costs are provided in the Appendix
6
EXPERIMENTAL RESULTS
6.1 MAIN RESULTS
Table 1: Performance comparison between proprictary and open-source models across various benchmarks. Low-L stands for Low-Level features (c.g., Block, Text, Position), and High-L stands for High-Level semantic fidelity. Best results are in bold.
Model
Size
Design2Code
ChartMimie
MMCode
InfiBench-V
Average
Low-I.
High-I.
Low-I.
High-I.
pass@
Acc
Proprietary Models
GPT-4o-mini
85.8
87.3
68.4
68.5
12.2
71.9
65.7
GPT-4o
90.2
90.4
79.0
83.5
17.0
79.9
73.3
Open-Source Small Language Models
MiniCPM-V-2
8B
78.1
84.2
21.8
45.2
3.8
45.3
46.4
Intern VL3-8B
8B
85.3
87.6
43.1
47.2
6.8
66.1
56.0
Qwen2.5- Owen2.5-VL-7B-Imme
V----BB-nnsttucc
8B
83.4
87.6
39.5
38.3
5.3
54.0
51.4
Llama-3.2-115 BBB B--iIBB---ononn--e Vision-Instruct
11B
72.7
84.8
27.7
26.5
2.3
52.7
44.4
Intern
15B
82.9
53.9
11.4
70.5
VisCodex-8B
8B
90.1
74.8
74.1
11.0
72.1
Open- Open-Source Source Large Language Models
Owwn2.55VIL-3222. llva-onevisioon-qwend
Instrruct
33B
88.0
89.4
72.5
68.7
13.7
73.0
67.6
73B
75.2
55.8
5.7
64.7
56.5
Qwen225-vV--72--nnnm
73B
86.9
88.7
66.7
68.7
15.2
75.2
66.9
VL
78B
85.3
89.1
64.9
64.2
14.4
77.3
65.9
VisCodex-33B
33B
90.5
91.1
79.3
78.5
15.6
78.6
72.3
As shown in Table [ our models achieve state-of-the-art performance across all evaluated multimodal coding benchmarks. Our smaller model, VisCodex -8,, not only outperforms all open-source models in its size class (7-15B) but also surpasses the proprictary GPT-4o-mini, with an average score of 68.8. Our larger model, VisCodex-33B,, further establishes its superiority by achieving an average score of 72.3, which on par with the state-of-the-an proprietary model, GPT-4o (73.3). These results demonstrate that our VisCodex family sets new standard for open-source multimodal code generation.
Our models show exceptional strength in UI and chart understanding. On the Design2Code bench- mark. both VisCodex-88 (90.1/90.9) and VisCodex-33B (90.5/91.1) achieve scores comparable to or exceeding GPT-4o. On ChartMimic, models also secure the top positions among open-source models, demonstrating robust visual data translation capabilities.
6.2 ANALYSIS
Efficacy of the Model Merging, As demonstrated in Table 2 model merging yields consistent performance gains across all benchmarks and scales. At the 8B scale, merging improves De-
Table 2: Ablation model merging for VisCodex. "w/o model merge" denotes the variant without applying model merging strategy.
sign2Code (90.1 vs. 89.6). ChartMimic (74.8 vs. 73.4), and MMCode (11.0 vs. 6.8). The 33B model shows similar enhancements. The most significant improvements ChartMimic and MMCode confirm that this strategy effec- tively augments code-gent onde-generation capabilities while preserving visual understanding.
Methud
Design2Cude High-L.
ChartMimic Low-L.
MMCade
High-1.
pass@1
VisCodex-88
90.
90.9
74.8
74.1
11.0
model morge
VisCodex-33B
90.5
91.1
79.3
78.5
15.6
w/o model merge
89.7
90.7
78.4
77.4
14.4
Effect of Different Code LLMs in Merge.
As shown in the Table B we study how the choice of
the merged L.M affects performance. All code-specialized L.LSs present consistent gains across all
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Table Ablation on Backbone LLM choice. Per- formance when merging the same multimodal backbone with either general purpose or code- specialized LLM.
Table Performance comparison of model merg- ing vs. backbone replacement. The "Replace (1- stage)" strategy directly replaces the LLM back- bone in single stage. The "Replace (2-stage)" strategy first trains a projector, then fine-tunes the full MLLM (VTT, projector, and LLM).
general-purpose
Backbone L.M
Design2Code Low-L
ChartMinsi
MMCCode poss
High-1.
Low-L
Highh1
Boseline (Qwes225VVL.
83.4
87.6
39.5
38.3
5.3
Gencral Z.MM
Strategy
Design2Code Low-l,
ChartMimic High-l
MMCode
Qwenn.5 7B- Instruet
89.5
90.7
73.2
72.5
|
6.8
High-1.
Low-l.
pascet
JJM
Roseline
83.4
87.6
39.5
38.3
5,3
OpenThinkaa2-77 Qwen2.5
74.1
73.8
5.0
Replace stage)
88.7
90.7
70.4
69.2
11.0
75.1
Replace (2-stage)
Nemotron- 1.1-7B
90.1
90.9
74.8
74.1
11.0
Merge (Ours)
benchmarks compared to general-purpose LLM. Compared to the general LLM,, OpenTTiinker277B and Qwen2.5-Coder improve both Design2Code and ChartMimic, while Nemotron- 1.10- further boosts MMCode pass @1 from 6.8 to 1 11.0. These results indicate that merging with code-specialized LLMs is crucial for robust multimodal code generation, enhancing executable correctness while maintaining strong visual grounding and Ul-to-code translation.
Effectiveness of the Model Merge Strategy. To evaluate the effectiveness of our proposed model merge strategy compared direct backbone replacement, conducted comparative experiments using two distinct approaches: (i) directly replacing the L.M backhone of Qwen2.5-VI. 1-nn..5--VL----Issttere with Opeenoodeeasonini -eroonnnn-eemooooo--- 1.1-BB (Ahmad et 22025,, and (ii) employing the two-stage training procedure from LLaVA-Oue Vision (Li et al. 2024a), which initially trains the projector on BLIP-558K, followed by joint fine-tuning the VIT. projector, and LLM McD.
Our results indicate that the model merge strategy achieves overall superior performance across the evaluated tasks, as shown Table 4 It demonstrates particularly strong gains on visually-intena -ittensve benchmarks such as Design2Code and ChartMimic, where successful code generation heavily relies on accurate sisual-semantic aligoment. This because directly replacing the I.I.M backbone often disrupts previously learned visual grounding. In contrast, the model merge approach preserves these visual alignment abilities while simultaneously incorporating enhanced code generation capabilities. This confirms the effectiveness of model merging in maintaining multimodal comprehension and boosting performance in multimodal coding tasks.
Additional Analyses. Further results are provided in the Appendix, including comparisons with existing Web2Code datasets (Appendix [B.]), the generalizability of McD (Appendix B.2), and the generality of our code model merging strategy (Appendix B.3
6.3 CASE STUDY
We further conducted case studies to qualitatively compare the performance of VisCodex-8B against GPT-4o, Intern VL3-77V and Qwen2.5-VL-7B on the ChartMimic and Design2Code benchmarks. As shown in Figure 2 VisCodex-8BB consistently generates outputs that more closely match the ground truth in both chart reconstruction and HTML generation tasks, surpassing the fidelity of results produced by GPT-4o-mini and other open-source baselines. These observations underscore VisCodex-SB's s superior multimodal code generation capabilities. For additional case studies on MMCode, InfiBench-V, V, and further examples, please refer to Appendix H
7
RELATED WORK
7.1 MULTIMODAL CODE GENERATION
The ability of MLLMs to generate code has attracted increasing attention recent years. De- sign2Code Si et al. 2024) evaluates the HTML generation capabilities of MLLMs. Extending earlier datasets like WebSight (Laurençon et al. 2024 and Pix2Code (Beltramelli 2018), Web2Code YYnn) et al. 2024), Webcode2M Gui et al. (2025) provides webpage- co-dee dataset to improve HTML generation. Benchmarks like MMCode (Li et al. 2024b) and Human-V V (Zhang et al. (024b) focus assessing MI..Ms in algorithmic coding tasks that incorporate visual inputs. Similarly, Chart-
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VisCodex-8B (Ours)
Ground Truth
GPT-4o
Intern VL3-78B
Dwen2.5-VL-7B
Figure 2: Case study comparing VisCodex- -B and baseline models on Design2Code and ChartMimic tasks, demonstrating the superior multimodal code generation capabilities of VisCodex-885
Mimic (Shi et al. 2024) and Plot2Code (Wu et al. 2024) evaluate MLLMs' capabilities to translate raw data into scientific charts. ChartCoder (Zhao et al., 2025a) addresses chart generation explicitly through large dataset of 160k examples. Additionally, CodeV (Zhang et al. 2024c) integrates visual data to improve large language models' problem-solving abilities. Despite recent progress, to the best of our knowledge, existing work falls short of providing complete and unified solution to multimodal code generation.
7.2 MODEL MERGING FOR MLLMS
Model merging has become a widely used approach for integrating the capabilitics of multiple models within the parameter space. A basic method involves simple weighted averaging (Wortsman et al.)
2022), while more advanced strategies have been developed in recent years (Ilharco et al.) 2022
Matena & Raffel 2022, Jin et al. 2022 Yaday al. 2023 Bandarkar al., 2024). Recently, several studies have applied model merging to enhance the capabilities of multimodal large language models. For example, REMEDY (Zhu et al.
2025a) improves multitask performance and zero-shot generalization in VQA tasks. (Akiba et al. generation, while Chen al.] (2025) improve mathematical reasoning abilities. [i et al. (2025b) enable textual preference transfer by integrating text- based reward model into an MLLM, without additional training. Our study demonstrates that model merging can effectively endow MI.LMs with strong abilities in multimodal code understanding and generation.
2025) enhance Japanese language understanding and
8 CONCLUSION
In conclusion, we have presented VisCodex, unified multimodal framework that effectively in- tegrates advanced visual comprehension with sophisticated code-generation capabilities through novel task vector-based model merging strategy. By leveraging this efficient approach, VisCodex significantly enhances multimodal large language models without incurring the costs associated with full-scale retraining. We also introduced the Multimodal Coding Dataset (McD), a comprehensive resource comprising 598k diverse, high-quality instruction-tumm examples, along with InfiBench- V, a rigorous benchmark designed specifically for realistic multimodal coding assessments. Extensive experiments confirm that VisCodex establishes -ate-of-the-art performance among open-source

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Published as a conference paper at ICLR 2026
multimodal code generators, demonstrating capabilities competitive with leading proprietary models such as GPT-4o.
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A LLM USAGE STATEMENT
A large language model (ChatGPT) was used to aid and polish the writing of the paper, including minor grammar correction and language refinement.
B ADDITIONAL ANALYSIS EXPERIMENTS
COMPARISON WITH EXISTING WEB2CODE DATASETS
As demonstrated in Table 5 our dataset outperforms prior Web2Code corpora across all metrics. Compared to Webcode2M Gui et al. 2025) and Web2Code Yun et al. (2024), McD achieves the highest scores on both low-level Belock-Matees Text, Position, and Color) and high-level evaluation. The gains are especially notable on layout- metrics and visual fidelity, indicating that MCD provides more accurate structural alignment and visual grounding for Ul-to-code generation, while also improving semantic consistency.
Table 5: Performance comparison of MCD with WebCode2M and Web2Code the Design2Code benchmark.
Data
Block-Match
Text
Position
Color
CLIP
Baseline
85.4
95.8
77.3
75.3
87.6
WebCode2M
82.1
96.0
72.4
72.6
86.3
Web2Code
84.4
93.4
76.2
79.6
88.8
MCD
89.6
97.2
84.7
86.8
90.7
B.2 GENERALIZABILITY OF McD
To assess the generalizability of our dataset McD, we conducted supervised fine-tuning experi- ments strong open-source MLLMs: InternVL.3-8B (Zhu al. 2025b) and Ilava-llama3.1 2 (Zhang et al. 2024a). We evaluated the models on multiple established benchmarks, as shown in Table The results demonstrate that fine-tuning with McD consistently and significantly enhances the multimodal coding abilities of both base models. In particular, we observe substantial improve- ments in both the Design2Code and ChartMimic tasks, as well noticeable gains on the MMCode benchmark. These findings validate the robustness and strong transferability of McD across different model architectures and suggest its value as a general-puu cenrral-purpose resource for advancing multimodal code generation.
Table 6: Generalization performance of McD: Results of supervised fine-tuning Intern InternVL3-8B
and 1ova--lama3.1--1 1-8b across multiple multimodal coding benchmarks.
Model
Design2Code High
ChartMimic High
MMCode pass@
Low
Low
InternVL3-88 VL3-8B
85.3
87.6
43.1
46.6
6.8
Intern VI.3-8--SFT
88.2
89.9
72.6
70.4
7.6
Ilava-Ilama3. 11-ma...-8bbb
7.3
78.4
6.2
4.8
2.3
lava-llama3.1-8--SSB
82.8
90.5
70.7
68.2
4.2
B.3 GENERALITY OF CODE MODEL MERGING
To verify that model merging strategy is not limited LLMS with Qwen2.5 backbones, further conduct experiments using Ilava-llama3.1 1-8b (Zhang et al. 2024a). an MILM based on the Llama-3.1 (Dubey et al. 2024) architecture. Specifically, merge llava-Ilama3.1 1-8h with the code task vector (Toode) obtained from DeepSeek-Rl-
(DeepSeck-Al 2025). following
https::/huggiinffaa.
hwtp:/deeesspee/11111ee amma3. 1-8b
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the same procedure as in our previous Qwen2.5VL experiments. As shown in Table the merged model consistently outperforms the non-merged baseline across all multimodal coding tasks. The performance improvement is particularly significant the MMCode benchmark, where pass @ increases from 4.2 to 6.5. These results, which are consistent with our findings on Qwen2.5VI. demonstrate the general effectiveness and broad applicability of the code model merging strategy.
Table 7: Ablation on model merging for Ilva-Ilama3.1-81 and DeepSeek DeepSeek-R1-Distiile R1-Distill-Llamaa Results compare models with and without the merging strategy.
Method
Dosign2Code High-L
ChartMimic
MMCode pass@1
Low-L
Low-L
High-L
w/o model merge w model merge
82,8
90.5
70.7
68.2
4.2
83.3
90.7
70.9
68.8
6.5
B.4 POST-MERGE UNFREEZING ABLATION
To further investigate whether weight interpolation introduces any cross-modal distribution shift after model merging, we conduct additional ablation studies by unfreezing different subsets of visual modules during post-merge supervised fine-tuning. Specifically, we compare three tuning strategies: (1) tuning only the LLM backbone (our default approach), (2) additionally unfreezing the crosll projector, and (3) unfreezing both the projector and the ViT encoder.
As shown in Table unfreezing visual components does not lead consistent performance improve- ments across benchmarks. All variants yield comparable results on Design2Code, ChartMimic, and MMCode. This indicates that the merged model maintains robust cross cross-modal alignment without requiring further adaptation of visual modules. Consequently, our parameter-effin -effccieeee strategy of tuning only the LLM backbone remains both effective and computationally preferable.
Table Post-merge Unfreezing Ablation. Comparison of different tuning scopes. We report the performance when training specific components while keeping others frozen. LLM (VisCodex-8B) represents our default strategy.
Design2Code Low-level
ChartMimic Low-level
MMCode pass@1
Tunable Modules
High-level
High-level
Baseline (Qwen225-VIL-7B-mmee
83.4
87.6
39.5
38.3
5.3
LLM (VisCodex-SB)
90.1
90.9
74.8
74.1
11.0
LLM Projector
89.9
90.7
73.4
74.1
9.5
LLM Projector VIT
90.1
90.9
74.6
74.7
10.6
B.5 COMPARISON WITH LORA FINE-TUNING STRATEGIES
To further validate the superiority of our Model Merging strategy standard parfmeter-efficient adaptation methods, and to investigate the impact of tuning different modules, we conducted a comprehensive comparison with LoRA (Hu et al. 2022) fine-tuning. We established two distinet baselines:
LoRA (LLM)): Applying LoRA tuning exclusively to the language model backbone, serving parameter-efficient counterpart to our language-only tuning strategy.
LoRA (VTT + Proj I.LM): Jointly tuning the vision encoder, projector, and language model using LoRA. This setting was designed to test whether broader parameter adaptation (i.e., including the vision encoder) could bridge the performance gap.
As shown in Table 9 VisCodex significantly outperforms LoRA baselines, even when the vision encoder is tuned (VIT + Proj LLM), with substantial margins of 9.0% on ChartMimic (Low-L) and 4.2% on MMCode. While broader vision tuning offers slight gains over LLM-only LoRA, it still fails to match VisCodex. Crucially, since both SFT (Full) and VisCodex employ the same full fine-tuning strategy, our superior performance confirms that Model Merging initialization provides a robust "knowledge injection" of code capabilities that standard SFT initialization cannot replicate.
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Table 9: Comparison of VisCodex with Full Fine-tuning and LoRA tuning strategies on the MCD dataset. VisCodex consistently outperforms baselines, demonstrating the effectiveness of model merging initialization combined with full parameter tuning.
Design2Code Low-L
ChartMimic Low-L
MMCode pass@)
Method
Strategy
Tunable Modules
High-L
High-L
Base Model
Qwen2.5 S-on2.5VVV 5-LL-7B-----B---- I-Intruuctt
83.4
87.6
39.5
38.3
5.3
Standard Initialization (w/o Model Merge)
SFT (LoRA)
LoRA
LLM
87.7
89.2
64.0
SFT (LoRA)
LoRA
VIT Proj + LLM
88.2
89.6
65.8
67.0
SFT (Full)
Full
LLM
89.6
90.7
73.4
70.6
6.8
Ours (w/ Model Merge)
VisCodex-8B
Full FT LLM
90.1
90.9
74.8
74.1
11.0
B.6 DATA EFFICIENCY AND ROBUSTNESS IN LOW-RESOURCE SETTINGS
To investigate the potential of model merging in data-scarce scenarios and its efficiency in transferring prior knowledge, we conducted two sets of additional experiments: (I) training with strictly limited data volumes (Data Scaling), and (2) training without specific algorithmic reasoning data (Domain- Specific Scarcity).
Performance under Data-Scarce Conditions. To evaluate the model's performance when overall training data limited, we trained VisCodex-8B randomly sampled subsets of the MCD dataset: 1 %% (6k samples), 5% (30k samples), and 10% (60k samples). We compared our model merging strategy against the standard fine-tuning baseline (w/o model merge).
Table presents the results. Our model merging strategy consistently outperforms the baseline across all data scales. Notably, in the extremely low-resource setting (1% data), the standard approach suffers from catastrophic forgetting in reasoning tasks, with MMCode performance dropping significantly from 5.3 (Zero-shot Baseline) 3.4. contrast, VisCodex effectively retains the baseline reasoning capability (5.3) due to the strong prior injected via model merging. Furthermore, even with only 5% of the data (30k), VisCodex achieves robust performance improvements, highlighting the significant data efficiency gained from the merged code priors.
Table 10: Performance comparison on limited training data (Data Scaling). We report the results of training on 1%, 5%, and 10% subsets of the MCD dataset.
Design2Code Low-L
ChartMimic Low-L
MMCode pass@1
Data Scale
Method
High-L.
High-L
0% (Baseline)
Owen2.5-VLL-BB-Instee
83.4
87.6
39.5
38.3
5.3
w/o model merge
87.9
89.5
60.1
62.1
3.4
(6k)
w/ model merge (Ours)
89,2
89.5
61.6
63.0
5.3
w/o model merge w/ model merge (Ours)
88.9
89.8
68.2
68.6
3.8
5% (30k)
89.1
89.8
70.2
70.7
5.3
model merge
89.4
90.1
69.5
3.8
10% (60k)
model merge (Ours)
89.8
90.4
70.6
71.0
7.6
Efficiency in Domain-Specific Data Scarcity. To simulate a scenario where task-specific data unavailable. we conducted an ablation study by removing the entire "Algorithm" category (129k samples) from the MCD dataset. Crucially, the "Algorithm" category in MCD consists of text-only code problems. Since MCD does not contain multimodal algorithmic data begin with, removing this category implies that the model is fine-tuned without any exposure to algorithmic reasoning data (neither text-only nor multimodal).
As shown in Table [T] when explicit algorithmic training data is absent, the standard SFT approach (w/o model merge) fails generalize the multimodal reasoning task (MMCode), resulting in low
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score of 3.4. However, VisCodex achieves score of 6.8 even without seeing any algorithmic training samples. These results validate that our model merging strategy significantly enhances efficiency by leveraging injected code reasoning capabilities, making the model robust even when specific domain data is entirely absent.
Table 11: Ablation study on removing "Algorithm" training data (Efficiency in Domain Specific Scarcity). The models are trained on the MCD dataset excluding the Algorithm category.
Method
Design2Code Low-L
ChartMimic
MMCode pass @
High-L
Low-L
High-L
w/o model merge
89.3
90.3
73.2
70.5
3.4
w/ model merge (Ours)
90.0
90.4
74.2
74.6
6.8
B.7 STATISTICAL SIGNIFICANCE OF PERFORMANCE GAINS AT SCALE
To address concerns regarding whether the performance improvements observed in larger models (specifically the 33B variant) are statistically significant merely attributable random variance, we conducted a rigorous robustness analysis. We performed independent inference runs for both the merged model and the standard fine-tuned baseline across all benchmarks. To account for generation stochasticity, we utilized a sampling temperature of = 0.6.
The results, summarized in Table 12 demonstrate that the proposed model merging method con- sistently outperforms the baseline across all evaluated metrics. The performance gains range from +0.5 +1.4 points, with low standard deviations (a I 0.29), indicating high stability. Furthermore, we conducted paired I-tests to quantify significance. The resulting p-values range from 1.4 x 10-1 to 2.6 x 10-7, which are orders of magnitude below the conventional a 0.05 threshold. These findings confirm that the advantages of model merging remain robust and statistically significant even at the 33B scale.
Table 12: Statistical significance analysis of the 33B model across 5 independent runs (T 0.6). We report the Mean + Std. The "w/ model merge" method consistently outperforms the baseline with statistically significant gains across benchmarks (p> < 0.05).
Method
Design2Code Low
Design2Code High
ChartMinic Low
ChartMimic High
MMCode pass @1
w/o model merge
89.68 + 0.08 90.56 + 0.05
90.62 + 0.08 91.14 = 0.05
78.36 + 0.11 79.74 + 0.29
77.32 + 0.13 78.66 0.11
14.38 = 0.04 15.68 + 0.18
w/ model merge
p-value
2.6 10-7
8.8 10-6
10-4
1.5 10-7
4.2 10-5
B.8 NECESSITY OF VISUAL GROUNDING: COMPARISON WITH STANDALONE CODE LLMS
To investigate whether the performance gains of VisCodex stem primarily from the inherent strength of the code branch rather than true multimodal fusion, we evaluated several state-of-thh-ar 7B-scale Code LLMs as standalone agents across our multimodal benchmarks. Specifically, we tested 1eoel25--oodrr-B---nn
and OpenThinker2-7B using the same input prompts (text description + image placeholders) as the multimodal models.
DeeeReasonnng-Nemorroo
As shown in Table 13 despite their exceptional text-only programming capabilities, these standalone Code LLMS perform near-zero on tasks requiring visual grounding. The near-zes zero scores are not due lack of coding ability, but rather because these models lack visual encoder and cannot process the essential image inputs.
For instance, on DESIGN2CODE and CHARTMIMIC, which heavily rely interpreting visual UI elements and chart data, the code models fail to generate meaningful outputs, achieving negligible scores (e.g., 0.0 on DESIGN2CODE Low-Level metrics).
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Similarly, on INFIBENCH-V V. the accuracy hovers around 1.5%, confirming the models cannot solve the problems without visual context.
In contrast, the base VLM (Qwen2.5-VL-7B-i)) -IIstrucc) demonstrates reasonable visual understand- ing (83.4 on DESIGN2CODE) but lacks advanced code reasoning capabilities (5.3 on MMCODE). VisCodex-8B significantly outperforms both the base VLM and the standalone Code LLMs across all metrics. This empirical evidence confirms that neither visual ability alone nor code ability alone is sufficient for these tasks; the performance improvements are driven by the effective fusion of visual perception and code reasoning within our framework.
Table 13: Performance comparison of VisCodex against standalone Code LLMs. "Low" refers to Low-level visual metrics (Block/Text/Posicn Tttps//eteooosio//oollllll match), and "pass @ 1" refers to reasoning accuracy. The results demonstrate that strong code models alone cannot solve multimodal tasks without visual grounding.
Model
Type
Design2Code (Low-Level)
ChartMimic (Low-Level)
MMCode (pass@1)
InfiBench-V
(Acc)
Qwen2.5 Sren2.5-Coder 7B- Instructt canncoodoccsonnnnneeeoooooo -emoorronn- OpenThhiker2-7BB
Code LLM
6.1
1.5
Code LLM
0.2
2.8
1.5
1.2
Code LLM
0.0
2.4
1.9
1.2
Qwen2.5-Ve VL--7B-Insstuuu VisCodex-8B (Ours)
Base VLM
83.4
39.5
54.0
Merged VLM
74.8
11.0
72.1
B.9 COMPARISON WITH SPECIALIZED CHART MODEL
To further validate the effectiveness of VisCodex against specialized multimodal code models, we conducted a direct comparative analysis with ChartCoder (Zhao et 2025a). sfate-of-thee-rr model explicitly optimized for chart-to-code generation. As shown in Table VisCodex-8B consistently outperforms the specialized ChartCoder across all evaluated benchmarks.
Even the domain-specifir ChartMimic benchmark, where ChartCoder is specifically tuned, Vis- Codex achieves superior performance (74.8 vs. 72.5 on Low-Level metries and 74.1 vs. 74.0 on High-Level metrics). Furthermore, generalized tasks such Design2Code and InfiBench-V V, VisCodex demonstrates a significant advantage (c.g.. 90.1 vs. 36.7 on Design2Code Low-Level). These results highlight the core advantage of VisCodex: it functions unified multimodal code generator that matches or exceeds the performance of domain-specifie specialists while maintaining robust versatility across diverse coding tasks.
Table 14: Performance comparison between ChartCoder and VisCodex-8B across multimodal coding benchmarks. VisCodex outperforms the specialist model its specific domain (ChartMimic) while maintaining significantly higher performance on general tasks.
Design2Code Low-level
ChartMimic
MMCode pass@1
InfiBench-V
Model
High-level
Low-level
High-level
Acc.
ChartCoder (Specialist)
36.7
82.7
72.5
74.0
2.7
32.3
VisCodex-8B (Ours)
90.1
90.9
74.8
74.1
11.0
72.1
B.10 SENSITIVITY ANALYSIS OF MERGE COEFFICIENT À
To address concerns regarding the heuristic selection of the balancing factor À and to evalu- ate the robustness of our model merging strategy, we conducted a comprehensive sensitivity analysis. We evaluated the performance of VisCodex-8B across range of mixing coefficients E {0.0, 0.7, 0.8, 0.85, 0.9]. Recall that according to Equation 44 x controls the weight of the vision-language task vector (Tetm). while (1 X) controls the coding task vector (Teade).
As presented in Table 15 the merged model exhibits high degree of robustness rather than relying narrow heuristic optimum:
Broad Performance Plateau: For values between 0.7 and 0.9, visual understanding capa- bilities (measured by Design2Code and ChartMimic) remain consistently high, with minimal
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variance. This suggests that the semantic directions of the two task vectors do not destructively interfere.
Reasoning vs. Vision Trade-off: As decreases (increasing the influence of Toode), we observe steady improvement in algorithmic reasoning, with MMCode pass@ increasing from 7.2 to 11.0.
Necessity of Multimodal Alignment: Critically, setting 0.0 (effectively using only the code adaptation shift) results in significant performance drop on visually intensive tasks (c.g.. ChartMimic average score drops from 74.5 69.8). This confirms that the VLM task vector (Telm) provides essential multimodal alignment that cannot be supplied by the code model alone.
Table 15: Sensitivity analysis of the merge coefficient on VisCodex-88 performance. The "Avg" columns represent the average of Low-Level and High-Level scores for the respective benchmarks.
Design2Code (Avg)
ChartMimic (Avg)
MMCode (pass @ 1)
0.9
90.6
73.5
7.2
0.85
90.5
74.1
7.6
0,8
90.5
74.2
8.0
0.7
90.5
74.5
11.0
0.0
89.7
69.8
11.0
B.11 ROBUSTNESS ANALYSIS UNDER VISUAL DEGRADATION
To assess the robustness of VisCodex under degraded visual conditions, we constructed Composite Noisy Dataset derived from the Design2Code benchmark. For each sampled instance, we randomly applied either Gaussian Blur or Low-Resolution Downsampling to simulate realistic low-quality images found in real-world scenarios. We evaluated both our model and the baseline under this mixed-noise setting.
As shown in Table 16, VisCodex-8B exhibits stronger resilience to noise compared to the baseline. While the baseline Owen2.5-VLL77--Imsee Instruct loses 0.8 points on High-Level metrics, VisCodex drops only 0.2 points. Furthermore, Low-Level metrics, under noisy conditions, significantly outperforming the baseline's original performance (83.4). These results indicate that merging code priors does not weaken visual robustness; rather, it appears to stabilize structural inference even when visual details are degraded.
model maintains a high score of 89.6 even
Table 16: Robustness analysis on the Composite Noisy Dataset (Design2Code). The "Noisy" condi- tion includes random application of Gaussian Blur Downsampling. Aindicates the performance drop under noisy conditions.
Model
Condition
Low-Level
High-Level
Qwen2.5-VL-7BBInns- Qwen2.5-VL-77.
Original
83.4
87.6
Noisy
82.7.o7
86.8
VisCodex-8B
Original
90.1
90.9
VisCodex-8B
Noisy
89.6.05
90.7.a.
B.12 IMPACT ON GENERAL VISUAL QUESTION ANSWERING
A potential concern with model merging is catastrophic forgetting, where enhancing code capabilities might degrade general visual understanding. To evaluate this, we assessed the model on three general
vision-language benchmarks: TextVQA (Singh et al. 2019). CharlQA (Masry et al.) 2022). and GQA (Hudson & Manning 2011)).
the performance differences between VisCodex and the baseline are minimal (< 1.3 on TextVQA and < 0.8 on GQA) and fall within typical variance observed in model merging studies. This indicates meaningful catastrophic forgetting. Crucially, these minor fluctuations acceptable trade-offs given the substantial absolute gains achieved across multimodal code-generation tasks (e.g., +6.7 points Design2Code, +35.3 points on ChartMimic, and +5.7 points MMCode).
As presented in Table 17
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is also worth noting that while general TextVQA drops slightly, domain-specifie UI text recognition (measured by Design2Code Low- LeveL metrics) actually improves significantly from 83.4 to 90.1.
Assessment of Catastrophic Forgetting on general VQA benchmarks. The slight decreases Table 17:
in performance are negligible compared to the significant gains in coding tasks.
Model
Tex
ChartQA
GQA
Baseline (Qwen2.5-WL--7-IInstee
84.53
93.96 92.96
60.36
Model Merge
83.23
59.65
C TRAINING PARAMETERS AND TRAINING COST
All models are trained on our eestruction-tuning dataset of 598K examples from McD. We employ the A optimizer with 10% linear warm-up followed by cosine learning rate decay. The maximum learning rate is set to x 10-5, with batch size of 128 and a maximum sequence length of 8K tokens. Training the 8B model for two epochs takes approximately 16 hours 8 nodes, each equipped with 8xA100 100 GPUs with 40 GB VRAMs. Training the 33B model under the same setup takes approximately 2 days.
D HUMAN EVALUATION
To assess the alignment between automatic evaluation and human expert judgment, we randomly sampled 100 questions from InfiBench-V and selected five MILMs for evaluation: GPTT-4-mini, VisCodex-33B, VisCodex-8B, Owen2.5-VLL77--mmn and -eeee..5-L--32--mmmee was tasked with generating responses all sampled questions, resulting in a total of 500 model outputs.
Each model
Annotator Details and Protocol. We employed three annotators, all of whom are graduate students in computer science with strong domain expertise relevant to the benchmark tasks. Although all annotators are non-native English speakers, they are proficient in English and familiar with the style and requirements of the benchmark questions.
Before the main evaluation, the annotators underwent comprehensive training and calibration process, including practice with a subset of the dataset, review of the evaluation criteria, and targeted feedback to ensure consistent understanding of the annotation standards. final readiness test was conducted to confirm aligmment among annotators prior to the main evaluation phase.
During evaluation, all meder-geneeatted responses were anonymized and presented in standardized format, removing all identifying information about the originating model. Annotators indepen- dently evaluated the samples without communication or influence from others, ensuring impartiality throughout the process.
For each question, the annotators were provided with the question, the model-gennerted answer, and the accepted SterkOverflow answer as reference. Each annotator was asked to indicate whether the model's ' answer correctly solved the problem (1 for correct, for incorrect).
Automatic Evaluation. For the automatic InñiBench-V V evaluation, we adopted the same metrics as described in the main text, assigning a score the range 0-100 to each response. For the purpose of agreement analysis with human annotations, we further mapped these scores to binary "pass" or "fail" labels according to a defined threshold. This binarization enabled direct comparison between the automatic evaluation and the binary judgments provided by human annotators.
Agreement Analysis. Table 18 reports the pairwise agreement ratios (as MT-Bench Zheng et al. 2023) among InfiBench-V and the human annotators, as well as between human annotators themselver vee.. On average, InfiBench-V achieved an agreement rate of 86.07% with human annotators, which is higher than the inter- anter-annotttor agreement (79.33%). Notably, the agreement rate of InfiBench- is comparable to that reported in previous works (Li et al. 2024c) (85.1%) 1)), demonstrating the reliability of our evaluation method.
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Table 18: Agreement ratios between InfiBench-Y - and human annotators, as well between annota- tors.
Judge
A-1
A-2
A-3
Average
InfiBench-V
87.00%
87 87.00%
84.20%
86.07%
A-1
80.80%
79.20%
80.00%
A-2
80.80%
78.00%
79.40%
A-3
79.20%
78.00%
78.60%
Threshold Sensitivity Analysis To rigorously determine the optimal mapping from continuous (-100) scores to binary pass/fail labels, we conducted comprehensive threshold sweep. We evaluated thresholds ranging from 0 100 with step size of 5. For each threshold, model response with a score S 2 was classified as a "pass," and the resulting labels were compared against human expert annotations calculate the agreement rate. As shown in Table T9] the agreement increases with the strictness of the threshold, peaking at 86.07% % for thresholds of 80 and 85. Based on this sensitivity analysis, we selected 85 as the decision boundary to maximize alignment with human judgment.
steadily
Table 19: Threshold sweep analysis for InfiBench- V. We evaluated agreement rates between automatic scores and human annotations across thresholds from 0 to 100. The highest agreement (86.07% is achieved at thresholds of 80 and 85.
Threshold
Agreement (%)
Threshold
Agreement (%)
Threshold
Agrecment (%)
0
68.29
35
75.54
70
85.40
68.90
40
77.85
85.91
10
68.90
45
78.39
80
86.07
15
69.31
50
81.98
85
86.07
20
69.92
55
84.01
90
85,40
25
73.10
60
84.62
83.33
30
73.64
65
85.40
100
81.98
E
DATA STATISTICS
E. DATA STATISTICS OF MCD
To ensure fair evaluation and prevent data contamination, we performed deduplication between our dataset and the evaluation benchmarks using SimHash (Manku al.) 2007), removing any data with a similarity score greater than 0.9 to the benchmark test samples. After deduplication, the resulting dataset contains total of 598k examples across four domains: HTML,, Chart, QA, and Algorithm. Table 20 presents the statistics the dataset, including the number examples and the average token length (with standard deviation) for each domain.
Table 20:
Statistics of the McD by domain. For each domain, the number of samples and the average token length (mean & standard deviation) are reported.
Data
HTML
Chart
QA
Algorithm
Size
200k
210k
59k
129k
Avg Length
632+144
551+190
1022+776
969+321
E.2 DISTRIBUTION OF SELECTED BENCHMARK QUESTIONS IN INFIBENCHIV
presents the detailed distribution of questions in InfiBench- V across various programming domains and languages. The benchmark consists of five main categories: Front-End Back-Ei End, Data Science & Machine Learning (DS & ML). IT Operations (IT Ops), and Mobile & Desktop development, covering total of 13 programming languages and 322 carefully curated questions.
Table 21
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Table 21: Distribution of InfiBench-V questions across programming domains and languages.
Category
Language
Count
Category Total
CSS
30
Front-End
I1TML
39
100
JavaScript
31
Java
30
PHP
24
Go
Back-End
75
Ruby
Rust
6
C++
5
Python
90
DS ML
R
5
IT Ops
Bash
11
11
Mobile Desktop
Dart
41
41
Total
322
322
E.3 IMAGE QUALITY ANALYSIS OF INFIBENCH- V
To address concerns regarding the robustness of InfiBench-V against realistic visual noise, we conducted quantitative analysis of image quality across all 322 benchmark samples. This analysis focuses on image resolution and clarity (measured via the Variance of Laplacian) to ensure the benchmark accurately reflects real-worke scenarios.
Resolution and Blurriness. As shown in Table 22] the dataset retains natural distribution of image qualities found in developer communities. Specifically, 10.56% of the samples are low-resolution (< 50k pixels), typically representing small UI elements or cropped error messages. Additionally, 4.66% the images are detected as blurry (Laplacian variance < 100), with extreme cases scoring as low as 1.28. This confirms that InfiBench-V includes challenging, low-quality visual inputs, testing the model's ability to reason under imperfect conditions.
Verification of Consistency. Despite the variation in image quality, the visual-text consistency is guaranteed by the source. We strictly filter Stack Overflow questions with an Accepted Answer, ensuring that the provided maages-regardless of their resolution contain sufficient information for human experts to solve the problem.
Table 22: Image quality statistics of InfiBench-V. V. The presence of low-resolution and blurry images confirms that the benchmark evaluates robustness against real-world visual noise.
Metric
Value
Total Samples
322 918 x 552 10.56%
Average Resolution
Low Resolution 50, 000 pixels)
Blurry Images (Laplacian Var < 100)
4.66%
Extreme Blur Case (Min Variance)
1.28
E.4 CHART CATEGORY STATISTICS
We provide statistical analysis of the chart categories contained in the Chart-to-Code portion of the MCD dataset. The chart data spans a broad range visualization types, including common statistical plots (e.g., bar, line. pie). multi-dimensional charts (e.g., treemap, heatmap, violin). and specialized forms such as candlestick, quiver, radar, and density plots. Table 22llee3rsspsss the percentage distribution across all 28 chart categories.
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Table 23: Percentage distribution of chart categories within the MCD dataset (206,000 chart samples).
Category
bar
pie
line
radar
3d
arca
combination
Quiver
scatter
box
Percent (%)
15.84
12.06
10.02
4.36
4.34
4.14
4.04
3.63
3.42
Category
violin
hearmap
troemaa
candlestick
bubble
Tunnel
Percent (%)
3,03
3.03
3,03
3,03
2.95
2,56
2.17
2.17
2.10
Category
ring
graph
errorbar
error-pout
unset
histogram
density
Percent (%)
2,10
1.89
1.24
1.24
0.93
0.93
F ANALYSIS OF MODEL MERGE
F.1 TASK VECTOR PARAMETER SEAMETER--SACEEE ANALYSIS
Layerwtte
Cosine Similarity
6.6
Transformer Layer (0-27)
(a) 8B Scale: Cosine similarity between Telm and Tcode
Gleaal Rean 00371 Layie Costie
35
Layer
(b) 33B Scale: Cosine similarity between Telm and Tcode
Figure 3: Layer-wise cosine similarity analysis of the task vectors used to construct VisCodex. (a) illustrates the orthogonality at the 7B scale (between Diven2.5-VL-7B--Insse and (b) illustrates the orthogonality at the 33B scale (between Qwen2.5- Owen2.5-VLL332B VL--3L--Insttneee and the Code LLM). The consistently low similarity scores across all layers confirm that the parameter updates for visual grounding (Totm) and code reasoning (Toode) occupy disjoint subspaces, minimizing bias accumulation during merging.
and the Code LLM),
To understand how the two task vectors interact during model merging, we conduct a layer-wise geometric analysis of the vision-langune task vector (Tetm) and the coding task vector (Teode). ). For each transformer layer I, we compute their cosine similarity:
Film T
S cos coos(Titmm To Toode)
(5)
Figure reports the cosine similarity for both the 8B and 33B scales. Across layers, the cosine values remain consistently small, with global means 0.026 for the 8B model and 0.009 for the 33B model, indicating that the two task vectors exhibit only weak directional correlation in parameter space. This suggests that the update directions induced by visual- languaee training and rode-neasoning training differ substantially across the depth of the network.
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In addition to directional similarity, also examine the layer-wise magnitudes of the task vec- tors. On average, Telm has a substantially larger norm than Trode. with mean values of 48.31 and 18.40, respectively, yielding
average magnitude ratio of 2.69. While vector magnitude does not directly correspond to functional importance, these measurements provide complementary geometric information about the relative strength of the updates contributed by each task.
Taken together, the low cosine similarity and the observed magnitude relationship indicate that the Iwo task vectors influence the parameter space distinct and non-overwriting ways. Although this geometric analysis does not fully characterize the functional interactions between the two tasks, offers supportive evidence consistent with our empirical finding that linear merging can retain capabilities from both vision- languaee and code-reasnning domains.
F.2 REPRESENTATIONAL SIMILARITY ANALYSIS
Layer-wise Representational Similarity Analysis (CKA)
1.00
CK1 Smmlarit
0.85
0.75
Deep Integrofisn (128-1285)
Processing Hobustnees
0.70
Beac VLM Oake LLM (Baniaze Meeged ve Daos VIN (Teah Only.
Output Ri-allyymmet
Nocped vs. Gode (Tss Only!
Mecged vs. Base VLM (iaiiolaal
14
16
18
24
26
Transformer Laper Index
Figure 4: Layer-wise Representational Similarity Analysis (CKA). The plot illustrates three key phases: (1) Visual Processing Robustness to Parameter Shifts early layers under multimodal inputs, (2) Deep Integration of code reasoning in layers 20-26, and (3) Output Interface Re-alignment the final layer.
To better understand the mechanism behind our model merging strategy, we conduct a layer-wise Representational Similarity Analysis (RSA) (Kriegeskorte al. 2008)) using Centered Kernel Alignment (CKA) (Kornblih et al. 2019). We compare the internal hidden states of the merged model against its source models the Base VLM (ttOwwee..-VL-----nnniitie
and the Code LLM Boooodeeaannnn-Neeeee cirtnntroo-111-750 1.1-BB). We analyze representations using input samples drawn from the MMCode (Li al.) 2024b) under settings: (1) Text-only Inputs to probe reasoning integration, and (2) Multimodal Inputs to investigate visual processing stability. The results are illustrated in Figure
Integration of Reasoning Capabilities (Layers 20-26). A critical question regarding model merging is how distinct capabilities are combined deeper layers. Our analysis shows that under text-only reasoning prompts, the Base VLM and Code LLM exhibit representational divergence in deep layers, with CKA similarity dropping to the 0.83 0.91 range. In this context, the merged model maintains an extremely high similarity to the Base VLM (> 0.97) while simultaneously exhibiting increased similarity to the Code LLM (> 0.86) compared to the baseline. This suggests that rather than creating distinct new "bridge" representation, the merging process effectively shifts the VLM's manifold towards the Code LLM's reasoning subspace without disrupting its original semantic continuity. This injection of code priors likely contributes to the data efficiency observed in Appendix B.6, where the merged model facilitates robust reasoning capabilities even the absence of explicit algorithmic training data.
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Visual Processing Robustness to Parameter Shifts (Layers 3-10). When processing multimodal inputs, we observe notable trajectory in the shallow-to-middee layers (Layers 3-10), where the simi- larity to the Base VLM drops to approximately 0.78. This divergence likely reflects the interference introduced by the Code LLM task vector, which was trained solely on text and lacks alignment with visual tokens. Crucially, this divergence is temporary; representations successfully converge back to the VLM's semantic space in deep layers (> 0.97 at Layer 20+), This pattern demonstrates the intrinsic robustness of the VLM backbone: effectively tolerates the parameter shifts induced by the code task vector in early layers and recovers the necessary visual semantics for final reasoning. This resilience aligns with the findings in the sensitivity analysis (Appendix [B.00,, where high visual performance is maintained despite these representational perturbations.
Output Space Alignment (Layer 27). At the final layer, the model shows a decisive alignment back towards the Base VL.M distribution (similarity stays high at 0.91), while similarity to the Code LLM decreases. This behavior mechanistically consistent with the observation in Appendix F. where the VLM task vector has significantly larger magnitude than the Code task vector. The dominance the VLM direction at the output layer ensures that the enriched internal representations projected back into the VLM's enstruction-follwimin subspace. This alignment preserves the model's 's conversational interface and general multimodal capabilities (e.g., TextVQA), mitigating catastrophic forgetting observed in Appendix B.12
F.3 COMPARISON WITH BACKBONE REPLACEMENT STRATEGY
To further validate the superiority of model merging strategy over the direct backbone replacement baseline (as discussed in Table 4), we conducted a CKA analysis on the Design2Code task. This analysis probes how well the models maintain representational alignment with the original VLM when processing multimodal inputs (images instructions). We compared the internal representations of the Merged Model against the Backbone Replacement Model (i.e., using the Code LLM backbone directly with the original vision encoder).
CKA Similarity w/ Original VLM
0.9
Alignment
0.7
0.6
Visual-Semantic Misalignment
0.4
Model Merging
Backbone Replacement
10
14
16
20
22
28
Transformer Layer Index
Figure 5: CKA Analysis on Design2Code. Comparison representational similarity to the original VLM. The Merged Model (Blue) maintains high alignment across all layers. In contrast, the Backbone Replacement Model (Orange) exhibits a significant Visual-Semantie Misalignment in the final layers (CKA drops to 0.39). This divergence indicates that without the initialization provided by model merging, the direct backbone replacement fails to preserve the critical alignment between the visual encoder and the language reasoning space.
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The results, illustrated in Figure reveal critical representational divergence in the backbone replacement strategy:
Visual Semantic Alignment (Merged Model):
The Merged Model maintains consistently high CKA scores (> 0.91) across all transformer layers, achieving an overall average CKA of 0.994. This confirms that merging process preserves the effective alignment between the pre-trained vision projector and the language backbone. Consequently, the model effectively interprets visual tokens and maps them to the appropriate semantic space without requiring extensive re-alignment training.
Visual-Semantic Misalignment (Backbone Replacement): While the Replacement Model shows high similarity shallow layers (attributable to the shared ancestry of the base models), resulting high overall average CKA of 0.939, it exhibits sharp divergence in the deep layers (Layers 27-28). where CKA scores drop to ~ 0.39. We identify this phenomenon as -isual-semantic misalignment: although low-level features processed similarly, the pure Code LLM backbone lacking the "mixed" initialization provided by merging fails to project these visual features into the correct instruction-follwiing subspace at the output stage.
fails
This analysis empirically demonstrates why Model Merging serves as a superior initialization strat- egy. Unlike backbone replacement, which introduces mismatch in visual-textual alignment and necessitates substantial computational resources to re-align the projector, Model Merging seamlessly grafts code capabilities while keeping the multimodal interface intact, enabling more efficient and robust fine-tuning.
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G PROMPT
G.1 PROMPT TEMPLATE FOR GPT-40 AUTOMATIC EVALUATION ON INFIBENCH-V
Prompt Template for GPT-40 Automatic Evaluation on InfiBench-V
You are a professional code assistant evaluation expert. Your task is to assess the quality of model-generated answer programming-related question by comparing it with the gold reference answer. The question includes both text and screenshot image (which may contain code, error messages, UI context). You must consider both modalities when evaluating the answer.
Please first read the question (text image), then read the model-generated answer, and compare carefully with the reference (gold) answer.
You must provide clear and detailed justifications before assigning scores. The scoring dimensions are weighted follows: Correctness (50 points) and Completeness (50 points).
Below is programming question-answering example.
[Question] {question]
The screenshot image provided alongside this question part the question context. You must use it to fully understand the problem being asked.
[Note]
(Screenshot Image]
[Model Answer] {model_answer)
[Reference Answer] (reference answer)
Please evaluate the model's answer based the following two dimensions:
Correctness (0-50): Is the answer factually accurate and technically correct? Does address the problem without errors misleading information? Completeness (0-50): Does the answer cover all critical elements present in the reference answer? Are any key details missing?
Justification:
1. Correctness: Yourr reasoning here>
Completeness: < Your reasoning here>
Scores:
Correctness: X/50
Completeness: X/50
Total Score: X/100
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Published as a conference paper at ICLR 2026
G.2 PROMPT TEMPLATE FOR IMAGE-DRIVER HTML GENERATION
Prompt Template for Image-Driven HTML Generation
I will provide you with an HTML image. Please design perfect HTML based on the style of this image.
Include all CSS code in the HTML file itself. Do not assume include any dependencies external files. There is no need to incorporate JavaScript for dynamic interactions. Ensure careful attention details such element sizes, text formatting, positioning, colors, and the overall layout. Ensure all content fully visible within single, non-scrollable screen, and delete redesign any parts that exceed this boundary. If the original style does not meet your needs, feel free to change it. (HTML Image]
Your response should include the complete content of the HTML and CSS file:
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Published as a conference paper at ICLR 2026
H ADDITIONAL EXAMPLES
H.1 MMCODE CASE I
Alice binary string. She thinks binary string bcautiful only doesa't contain the subsiring "010"
Question:
versa. print steps needed make Alice the string beautiful.
one step,
change
Example
010
She can change any one element and have beautiful string.
Sample Input
STDIN
Function
length of string
draigi now
0101010 b= 01100100"
Abr changing Inler
Sumple Output
Explamation
In this sample, b "0101010"
The figure on the right shows a way to get rid each instance of "010": Make string beautiful changing characters (6[2) and 6[5]).
Qwen2.5.Vi VT-77-nsrree-
beautifulBare www.iiiayyyrriggggg....
b 1ist (b)
For in Lange:len 21:
if blil *0" and bli '1" and bli K0
b /11
bli 1| KD"
return count
VisCodex-8B:
To solve the problem of making hinury string "heautiful" (i.e,, remuving all occurrences of the substring "010"), need to identify and
count substrings eliminate it.
given hinary string.
occurrence "010", determine minimal changes required
Here's 's how we can approach problem:
Identify Occurrences: Traverse through string and find all instances of substring "010".
2. Count Changes: For each found "010", determine the minimal number of changes required to break this pattern. Typically, changing either sullices.
middlle "1"
3. Sum Up Changes: Sum the changes for all identified "D10" substring to get the total number of steps required.
Let's implement this logic Python:
def beanr:iflllnnnnneeeeeee SuliinaySttinnnngggggggoo (b)
unimialize the counter for the number of changes
changes 0
Iterate over the string to find all occursences of "010"
waile <= len(b)
if b[i:it3] "610":
We found *010" pattern, increment the changes counter changes t=
Move Index past this pattern +=
elae:
Nove the character
+
return changes
Figure 6: Examples of MMCode problems that successfully solved VisCodex-8B but not by Dwenn2.--VL.77-Innttme.
H.2 MMCODE CASE 2
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You have most definitely heard the legend of King Arthur and the Knights the versions of this story proudly point out the roundness the Table is closely related tn Arthur's belief of equality among the Knights. That is a lie! In fact, Arthur's choice table conditioned by his childhood traumas.
Question:
Round Table.
foreed clean quadratic tables from young age after tournament in pick-up sticks' had been played on them. Afler the tournament, typically there would be bunch of sticks the table that da touch each other. In the spirit of the game, the organizers issued strict regulations for the table cleaners, More precisely, the sticks on the table need to be by one way that cleaners pull
In fact, Arthur
way towards edge of the rable closest to where they are currently sitting. They also mustn't rotate or touch the other sticks while doing (not even the odge points).
the
In this task, we will represent the table in the coordinate system with square that has opposite points in the coordinates (0, 0) and (10 000, 10 000). whereas the sticks will be represented with striight line segments that lie within that square We will assume that Arthrr is sitring the edge of the table lying on the a-axis. Then the movement of the stick comes down to translating the line segment along
off shown in the right image). It your task help Arthur determine the order of stick movements that moets the requireennts from previous paragraph.
path
z-axis
-L-2---VL--B--sttree
import sys
sys. sts.sndin..o rgs.stdin.recddiize rezdline
det s)lve()) int (ingut ())
For in zange (n)
(x1, yir x2, y2) map (int, input (1. sp.api (1) x2, y2)
la.eppend (x1,
1s.aort (key=l amboa x: [x[1] - x(3), x(011)
for (x1, y1, x2, y2) in 15:
ir yl -- y2:
append))
else: aps.append
print (*ans)
solve
x
VisCodex-8B:
semove_sticke_ete ooder sticks):
Create list of tuples where each suple is (index, xl, yl, x2, y2)
indexed_stickss [tt 1, x1, yl. x2, y2) 1, (x1, y1, x2, y21 enumerate (st ick))
Scrt the sticks based on the y coordnnate desceading order indexed_sticks sort (key=lambda
(-x[2], -x[411>
Extract indices of sorsed ssicks worted_indians [stick[0) for ssick in indexed_aticks;"
return ind
Example usage:
if __me_____ =- "_main"
import sys input ays. data Input ().aiit
n = int (d1ta:01)
sticks
index
range (n)
x1 int (d [dre
y1 -int [data[index 111
x2 :int (data ddatalindex 211
y2 int (data [index 3]1
aks.aapednd (x1, y1, y21)
index t-
result remove st diiks_order sticka)
print (" ", join (map(stz, result)):
Figure 7: Examples of MMCode problems that are successfully solved by VisCodex 8B but not by Vien2.5-VL--B--Inseee
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H.3 INFIBENCH- V CASE
numbers various create plot similar the one shown the image, depicting the abundance of these variants over time. am working Python, and would prefer to use matplodib, although other libraries also acceptable if necessary.
Question: dataset
Does Python offer any specilic function or tool for generaling this type of plot? Additionally. for this kind of plot, have encountered hefore hut having trouble linding relevani resources online!?
what the common
plat." Python, you create plots using pttplotibb library, Although there isn't la specific function called "area plo"" masplottin, you can use the stackplot ot function generate this type of visuiization
Response: type plat shown image commonly
plot"
Here's basic example how you
stacked using natploriib:
import matp matplottie lit pypplot pit
Ieport numpy as mp
Example data
"p.arange D-D +2021-03',
vazianti ap.rradoon condddonoooniiio zondiiinn (dates)
variant2 .ap.randon zondilen (dates)
veriant: -pp.randon. rand (datte)
+Sormelize data to sun to 1 for each day
total verizncl variant2 variant3
variantl tetal
variant2 total
variant3 /- tetal
Create stacked
pit.stackplot (dates, vorianti, variant2, variznee, labeis ('Veriant 'Variant "Variant 3r), alpha-0.8)
(oc.llgeadlooe/uppppo lott")
pit.xlabel ( Datef)
pit.ylabel Frequency
pit.titie Abundance Veriants over Time!)
pit..show[ ()
In dis ctappe, variantl, variant and variant are amay represemtnng the frequencies of different variants over time. You should replace these with your actial data. Make
data nomalized (ie, the variants for each time point should equal of 100% expressed percentage terms) before
plotting
If you prefer using pandas, you can also levenge its expabilities simplify data manipulasion ploting:
inport pandas as pd
inport metplot lib lipppyplot plt
Example data date
'Date's pd.dste_ (start"' 2021-01-01', perlode=60, tregera"),
*Variant ep. rg-randon,
2/ up-randon, (60)
"Variant3's rg.rrandon (60)
dt aed (dets)
df set i Ditee implace=True)
line
day
di cam (exissl], exix=0)
Plot
df.plot. talestaapheee 08.
pit.ylabe ('Fequenncy (R)
eitititlel Abundance of Varients Over Time!)
sht.
This appeochh simplifies handling of data, especially it't's already in pandes DataFrime format, Adjjstt the lsbels neatel your specific dataser.
Category: MI.-
Python
Evaluation Criteria: Keyword Matching & GPT-4o
Keywords: stackplot, netplciiii
Keyword Matching
GPT-lo Score: 100
Total Score: 100
Figure 8: A case of InfiBench-V from the DS & ML category.
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H.4 INFIBENCH- V CASE 2
in my macOS Temninal by following a guide modifying my -bash pire#t le file. After creating and saving the bash prof l, time I open the Terminal, encounter following enors as shown in the screenshot on right:
Question: latlempted lo change
Despite deleting the .bach.profile, the nsue persisis, Every command I enter followed
by these erross:
-bash: git: command not found
gt:
Ioand
-bash: /dev/null: Fermission denied
Here 10B00c---cccooeeeeeeeeoooooo
added bash.profile
export CLICOLOR=1
export T.8.0t.RRsG
perse_sit bianch
giz branch 2> /dev/null
sad 17" ["x]/e'
1s/v 1.817 (31)
export PSi="Ne (0) 35m->>
No (3 (3m 34m
Whe (0, 32m) 8 sparse_git tatoo_gtt______tt (rranchr)))) Vo [0) 3mm
Please help me resolve issues understand why these enros are occurring.
Response:
you'de encountering likely incoreet syntex your peshhpproii file. Let's addiesss
1. Incorrect Redirection Symbol: in parse.git Joranch function, msed instend of for redirecting to /cex/moll. The coreet
git branch 2> /dev/null
Spaces Your PS1 variable some spaces might
should look
export PS1 Vaa [Gy 15m6gt; figti 'ee (1; W [O; 32m). (parse_g www.eeggg..oo... t___t__baaaa branchhhnhhh No (02 37mm
Make sure there are no unintended spaces around special characters or within the escape sequences.
3. Check for Hidden Characters: Sometimes, copy eepy-pasting from certain editors wehsites introduce hidden characters, your bbash.pra pi them.
shows whitespace use command line 4. File Permissions: Ensure besh.proffll file has the correct permissions. You caa with:
contain any non geintable characters.
w.b.aahhpppp proffle
5. Recreate huch. If the preblem penists, try recraatnng the hhe.h.proti..... file manuilyy without copying and precing. Open a new terminal and type command directly
file.
restart your temminal should resulve errors you' seeing. problem continues. double-check other syntas eturs unexpected characters in the tile.
making changes,
Category: IT Ops Judge: GP'--4
Bash
Correctness:
The model answer cornectly identifies the primury isue with the itsch.protiii ie file, which the use of ITTL eniiies (Ggt.;) instead of the currecl characters This aligns with the reference answer.
The model also addresses the posential for non- prinraliaaleeee characters and file permissions, which, while not mentioned expliciity in the referesee answer. are reasonuble considerations given the syngtomm described.
Additionally, the model sugeests recreating the bast.protia protill file to ensure there are no hidden characters, which a practical trubleshuwiing step that could help reselve the persistent isues.
Overall, the model's suggestions are factually accurate and technically correct, direcily addressing the syntax emron..
2.
Completenes:
The reference answer concise and fucuses soiely on the issue of HTML entiiies while model answer peuvides a more comprehemsive uppeouch by ancluding addiiional coubleshooingg steps (checking for hadien characters, file pentissions, and recreating the files.
Although the model ancwer includes extra steps beyond the reference answer, i does not miss any vritical elements presem. the reference, Instead, j enlunces the response with additonnl useful advice for reooling the isme thonoghlly.
modal's complateness therefore high, as it covers key
reference answer further depth.
Correctaess: 30/50
Completeness: 50/50
Total Score: 100/100
Figure 9: A case of InfiBench-V V from the IT Ops category.
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H.S INFIBENCH- V CASE 3
wurking a game JavaScripi issue. The game involves player shonting target, hut there is an obstacle blocking the shot between the player and target. need to find the coordinates obstacle. Here the information have:
Question:
encounlering irigunomelryy
152. (2)
1. The starting coordinates of the shat are (111 U1
2. The ending coordinates of the shot are ( y2))
3. The distance from the starting coordinates to the ohstacle known.
How can calculate (x, y) coordinates of the obstacle along line the shot? Addition- ally, would appreciate efficient solation implemented in JavaScript. Pleuse refer to the atlached image for visual representation of the problem.
v1l
Response:
shot, you can use vector mathematics. starting point (x11, 2 ending point (#22, (22), and the disrance d from the starting point the obstacle, you can calculate the cooedinates of the obstacle follows: Calealate dinection
solve problem finding condinates obotacle along
( 2/ (122) ya
direction
(ing
91)
Nontalize the direction
direction that:
magnitude
v (irg
12 + (v2 21
unit direccioon
322 101
92
91
magnitude
magmitude
3. Multiply the unis direction the distance to find the displacement from the starting point obsiacle:
displasement (d wiit mnilciiiccion.s unit mimictiiit
4. Add the displacement vector the starting point to the coordinates of the obstacle:
obstacle #1 + displacsmenr,
obstacley dislacement,
Iere's how implement this in JavaSeript:
This cude will give you the coordinates of the obstacle along the line the shot, given the specified disiance from the starting point.
Category: Front-End
JavaScript
Evaluation Criteria: Unit Testing
Sample Test Case:
iopat: [*x1":0.0, "y1*=0_0, o, *22*:20.0, 2y20s0.0, "distaro"":S.00
expected: 15.0, 0.0),
actual: 15.9, 0.01, ...
(only partall cases abowa)
Total Score:
Figure 10: A case of InfiBench-V from the Front-End category.
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H.6 INFIBENCH-Y V CASE 4
Question:
am developing Hlnne application where want to draw square the screen the location
a click. squre should ventered click currently, being drawn scemingty random seinterEvemlllcaleesiiii
'event pointeeenn loalluuuusss the clisk coordinzess, and am passing these coardiaaees to Ceum draw the square. Below is Fluter code, which includes the "prnitsquure widget and the 'handleAdd Add' function from its pareut widget, "MySpciteaame"
posiions. using
sprite.square.dar
class SprizeSquare extcads NodeWithSize 1
Function handices
double letts
double zopz
Spritesquare chis.color, handeea this.top, this.lett)
Plagt
(size)
usetinteractionnnaaae treer
Roverride handicEvent (opritotoxEvent event) handlez (event))
Rovorride
Futurecvoids paint
aayoe
canvas, drawRact
foot. lleft, top, hoight widthl, Paint coclor color
handleAdd
handleadd (SpesteeeexEvent eventl
Final whisessuure spriteSquare?
sonnal Baxe 150. 50.01,
conet Color (ONFFF7ezZ))
hand eReenoeeS11:
(event. peintersvent woc.iPoosiiion.de
event pentt.poitterteee loca dyy)tion.dy)
kackkgoondd.c. www.achhhiiiiitttteearrrrao trues
Cin understand why not being drawn centered at the click location and how fix this issue?
Response:
The issue to expesiencing with the square not being drawn centered at the click location likely due the way re calculating the position of the square. in your explemenatioo, starting that rectangle to center the square at the click location, you need adjust the 'lett and 'top' values by subtracting half of the square's 's width and height, respectively. Here's how you can modity your code:
Reei.from.WW Wil' draw the rectangle, which the led and top coordinates the point rectangle. This top-left
specifled cocediatess than haing comtered
In "handleAdd" function, calculote 'lftt
this:
handlakdd EoxiivtoxEvvtt event)
rinel clickX event, wwent.pointceee. wow.:crwwent. wow.psssttsiiiiiii.oo.o. dX)
final elicky event, poincerEvent. oot.alooaatioo.dy.
square that is centered st the final left = clickX 25.0) /7 Balf of the squarets width 150.0 /
Calculate tap-lett coxner
rinel tog n click? 25.01 // Balf of sha square" height 150.0 /
_xhitesquare Spris48quara
ecast (30.0, 20.0). const Color (6xPE9919PF1, handiaRanoveSalf, left,
Log
backgreund.
addchild addcaii
[uhiicsquurel
relusa Leuw;
adjusting 'lett
valtes way, square will location. effectively moving
square ao position that centers the square around the click point.
Category: Mobile & Desktop Dari
Evalaution Criteria: GPT-440
Curreciness Score: 35/50
Completeness
Total Score: 60
Figure 11: A case of InfiBench-V V from the Mobile & Desktop category.
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Published as a conference paper at ICLR 2026
H.7 INFIBENCH- V CASE 5
Question: coneext a Ruby
application using 2.3.5 Ruby wooking
with three damabase tables: A, and C. Thee relatioshiips are tollows: table A has muhiipl associated records in table D. and talle also has multiple associated records in table B. There is direct relationsipp between tables C.
want to query records from the quay using the following eode:
include associated records tables
include
a.al1 [:condi tiona =>
I :id
id 1 :include => [:B]]
Hlwever. am unsure
table query. Despile extensive research,
solution. How can modify the quesy to include records from both tables B and when querying table A's
recorde?
Response:
To include asonciated records from bath tibles B and C when querying tible in Ruby on Rails application, you need to ensure Ouat the associations are set up
comrectly models. Assuming have the following associations:
Table has many
(e.g. hasunany 1bs).
Table Cas many recodss table (eg. Fas.nery 106s)
You can use euger loading with iinclude to load associated reconds from buth tables B and C, Ilere's 's how you can modify your query:
conditions
rid id sincllade
sbs w>
This you have haaamayy =ba,
In model U. you have belones to a and has nany cca, In model C. you have has mayy thss.
and is different, adjest Linel ude aecordingly. If there direct relationship berween and C, you include through make
if the association
association correctly detined your
Note: Since using Ruils 2.3.5 and Ruby 1.8.7, the syntax for euper loading slightly diffienent compored versions of Rails. Make models' associations are set up cocrectly to reflect these relationships.
Category: Back-End- Ruby
Evaluation Criteria: GPTT-40
Correctness (I-50): The model nnswer provides solation using Kails" enger loading feature with the siacl ude option in way that aligns with Rails 2.3.5 modification A. all (sconditiona
syntax. correctly assumes that acts internediary berween and C. The peoposed
:id w> id through This matches associations
},
valid include assoeiated racards from both
answer elabonaes on rssociations correct However, the reference answer is more concise and directly points to the usage of :include => :E w> ic without additionll assumption about model sctup. The model ancuer's explanation about acsociations, while conect, is nccessary reselve the specific Therefore,
depicted screanshot,
linked
models, which technically
issuc. Nonetheless, the high, albeit slightty vechose.
model's proposed query would work given the
the model's
2. Completeness (0-50): The model answer provides comprchensive response by explaining the under tying nsommptios about the model rosociations which associaiooss should
answer elaberniig This adBiional detail peovides a complete inderstanding of how the query interacts the model stmicrree While reference inswer
up the models. suceinct, model answer's is theroughnes in explaining potenial asociiaion configurations adds value und ensures that the user understands the prerquisites for the to address the question direclly
essental achieving the desired query beisavion. goes beyond the
function intended. Thus, the model anower complete but includes informatioon that, although helpful, strictly
Corvecness:: 45/50
Completeness: 45/50
Total Score: 90
Figure 12: A case of InfiBench-V V from the Back-End category.
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Published as a conference paper at ICLR 2026
H.8 FAILURE CASE
Despite achieving state-of-the-ee art performance, VisCodex exhibits limitations when handling highly complex 3D spatial relationships or information-dense UI elements. As illustrated in Figure 13 we identify primary failure modes in 3D spatial reconstruction (Row 1) and fine-grained UI content generation (Row 4), where the model struggles to precisely reconstruct coordinate connectivity or render detailed inner content.
Crucially, we observe that the backbone model, Bwve2.5-VL-7B-- Innstrcct, exhibits similar failure patterns on these challenging samples. Since VisCodex-8B built upon the Pwwen2.5-VL-7B I--ruutnstt architecture, it inherently inherits the visual perception capabilities and effectively the limitations of this foundation model. This implies that the current visual understanding bottleneck stems primarily from the base model rather than the merging strategy itself. Consequently, anticipate that future improvements in the visual capabilities of base models will directly drive further enhancements in our framework's multimodal code generation performance.
Ground Truth
VisCodex-8B (Ours)
GPT-4o
Qwen2.5-VL-7B
Failure case analysis on ChartMimic and Design2Code. The model struggles with 3D spatial structure and fine- ime-grained UI details. Notably, these perceptual limitations mirror those of the backbone model, Twee2.55VL--77 VL-7B-Instruec, indicating that VisCodex-8ll 8B's visual understanding bounded by its foundational architecture. Future advancements in base visual models are expected to mitigate these issues.
Figure 13:
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I TRAINING STABILITY AND THEORETICAL FOUNDATIONS OF TASK- VECTOR MERGING
We analyze both the empirical optimization behavior and the theoretical underpinnings of task vector- based model merging to demonstrate that VisCodex maintains stable training dynamics and operates within a theoretically sound merging regime.
I.1 TRAINING STABILITY AND Loss DYNAMICS
smoothed
Loss
2000
4000
6.000
8000
Training Steps
(a) Training loss of VisCodex 8B during SFT.
original
smosthed
0.45
0.35
0.25
0.20
0.15
Training Steps
(b) Training loss of VisCodex-33BB during SFT.
Figure 14: Smoothed training loss curves for VisCodex-8B and FisCodex-33B. Both models show stable, monotonic loss reduction throughout SFT, indicating that task-vector merging does not introduce gradient conflict destabilize optimization.
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Published as a conference paper at ICLR 2026
We examine the optimization dynamics of VisCodex-8B and VisCodex-33B during supervised fine-tuning (SFT), following the one-shot task-vector merge.
As shown in Figure 14, training proceeds smoothly for both model sizes, with spikes, oscillations, or irregularities. This confirms that the merged initialization forms a stable starting point for downstream training and does not cause accumulated bias or interfering gradients.
1.2 THEORETICAL FOUNDATIONS OF TASK-VECTON VECTOR COMPOSITION
Our empirical findings align with established theoretical results on model merging and task arithmetic.
Linear composition with minimal interference. Task vectors encode low-curvature directions in parameter space corresponding task-specific transformations. Prior work shows they can be linearly combined to transfer capabilities across domains without retraining and with limited interference (Ilharco et al. 2022). Fisher-weightte and interference-awe aware analyses further demon- strate that successful merging occurs when tasks modify disjoint or weakly overlapping parameter subsets (Matena & Raffel 2022 Yadav et al. 2023
Our case satisfies this condition:
near-orthogonality between Tvim and Toodes
smooth A-sensitivity curves (Appendix [B.10].
high deep-layer CKA similarity 0.97 after merging (Appendix F2
These observations indicate that VisCodex lies in the theoretical regime where task-vector composition expected to succeed.
Cross-domain ability fusion. Recent studies show that merging supports the combination of heterogencous skills- such as vision mathematical reasoning (Chen et al. 2025) or textual preference integration into multimodal models (Li et al. 2025a because the underlying capabilities tend to occupy separated parameter subspaces. Our results extend this direction: vision- languagee understanding and code reasoning also exhibit such structural compatibility, enabling reliable fusion via linear task-vector arithmetic.
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Reviews:
[{"review_id": "YR28liTqR8", "reviewer": "Reviewer_Xh1p", "summary": "The paper introduces VisCodex, a unified multimodal code generation and understanding framework. It combines newly curated training and evaluation datasets with a model merging strategy that fuses multiple task-specific models (e.g., vision-language and code-language) to enhance cross-modal reasoning. The method aims to balance specialization and generalization across visual programming, text-to-code, and code reasoning tasks. Experiments on multiple benchmarks demonstrate competitive or superior results to single-task fine-tuning.", "questions": "- What is the concrete benefit of model merging when extensive SFT is still required afterward?\n\n- Have the authors evaluated performance under data-scarce conditions to demonstrate merging’s potential efficiency?\n\n- For large models (33B), is the marginal gain statistically significant, or within noise level?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Solid and comprehensive contribution: The work offers not only a new training method (model merging) but also new datasets for both training and evaluation, which strengthens its empirical foundation.\n\n- Methodologically clear: the overall paper is well-written, organized, and easy to follow.\n\n- Breadth of evaluation: Covers diverse multimodal coding tasks, showing consistent, though not always dramatic, gains across small and medium-sized models.\n\n- Timely and relevant: Addresses the challenge of unifying multimodal reasoning (code + vision + text), which is a growing area of interest.", "weaknesses": "- Unclear advantage over standard fine-tuning: As shown in Table 2, model merging offers almost no improvement for large models (e.g., 33B variant), suggesting diminishing returns at scale. This weakens the claim of broad effectiveness.\n\n- Limited discussion on data-scarce scenarios: One key potential advantage of model merging could be in low-resource multimodal settings, yet this is not explored. It’s unclear whether the approach would still help when task-specific data is limited.\n\n- Lack of deeper analysis: The paper could have benefited from probing why merging helps (or doesn’t) at different scales — for example, via representational similarity or parameter-space studies.", "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": "R3jOGNM0sh", "reviewer": "Reviewer_66mV", "summary": "VisCodex proposes a unified multimodal framework that merges a vision-language model with a coding LLM to enable strong code generation from visual inputs. It uses task-vector based model merging to combine visual understanding and programming ability without full retraining, preserving both skills. The authors build a 598k-sample Multimodal Coding Dataset and introduce the InfiBench-V benchmark for realistic evaluation. Experiments show VisCodex achieves state-of-the-art open-source performance, rivaling proprietary models like GPT-4o.", "questions": "Could you further clarify why the direct SFT strategies underperform beyond visual grounding disruption, and provide more analysis or diagnostics to better understand what specific capabilities are degraded during tuning?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Introduces a model-merging based path for multimodal code generation, combining vision and coding expertise without full retraining, and expands the problem space with new data and benchmarks.\n\n2. Demonstrates solid empirical rigor, with extensive evaluations showing consistent performance improvements over strong open-source models and competitiveness with proprietary ones.\n\n3. Addresses a meaningful and underexplored capability, turning visual content into functional code and offering practical impact for applications like UI-to-code and chart-to-code systems and shaping future MLLM research directions.", "weaknesses": "1. The comparison to direct SFT strategies is limited in scope; while the paper includes one- and two-stage baselines, a broader evaluation (e.g., LoRA tuning on both vision and language modules) would strengthen the claim that merging is strictly superior for this setting.\n\n2. The dataset construction pipeline relies heavily on model-generated content (e.g., GPT-4o generated HTML and curated chart code data) but lacks detailed analyses of potential data bias, overfitting to synthetic structures, or failure modes on fully real-world screenshots/code artifacts; evaluating broader generalization and reporting error breakdowns would improve credibility.", "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": "K0QM7yslfP", "reviewer": "Reviewer_FB3k", "summary": "The paper proposes VisCodex, a task-vector model-merging method that injects a code LLM’s capability into a VLM by linearly combining task vectors in the language backbone while freezing the vision encoder and projector. It introduces a 598k-sample Multimodal Coding Dataset and a new benchmark, InfiBench-V.", "questions": "1. What exact threshold maps 0–100 to pass/fail on InfiBench-V? Please report a threshold sweep;\n2. Please add a post-merge unfreezing ablation to test for cross-modal distribution shift introduced by weight interpolation. At minimum: unfreeze projector only, unfreeze projector + ViT.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear, formalized merging recipe with explicit task-vector definitions and a single-parameter interpolation;\n2. The paper introduces a compute-efficient design, only the LLM backbone is merged or tuned but vision & projector are frozen;\n3. The authors introduce a dataset large, diverse MCD and a benchmark (InfiBench-V) targeting visually-rich programming questions;\n4. It reaches several strong numbers on Design2Code/ChartMimic, using a 33B model close to GPT-4o on average.", "weaknesses": "1. The paper shows example items evaluated by \"Judge: GPT-4o\" to 50/50 component scores, which confirms the setup, without disclosing the actual threshold or how it was chosen.\n2. The paper does include an unfreezing setup, but only for the replacement baseline. For the VisCodex, training freezes the vision encoder and projector and fine-tunes only the language backbone, and the paper does not report an ablation where these modules are unfrozen after merging.", "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": "cBvxhbCHLF", "reviewer": "Reviewer_Qdg3", "summary": "The paper addresses the limited multimodal code generation abilities of current MLLMs and proposes VisCodex, a framework that merges a vision language model with a code LLM through task vector based model fusion. Supported by the newly constructed MCD dataset and the InfiBench V benchmark, the approach achieves state-of-the-art performance among open-source models. Its main contributions lie in efficiently extending model fusion techniques to a new scenario and building high-quality data and benchmarks. However, the method offers limited novelty, the experiments are not sufficiently comprehensive, and several technical details remain unclear. The work requires additional key details and experiments to improve completeness, but it still carries certain academic and practical value.", "questions": "1. Baseline Comparisons with Specialized Multimodal Code Models\n Why were recent multimodal code models such as CodeV, ChartCoder, or Plot2Code excluded as baselines? These models directly target visual-to-code tasks and are more relevant than generic VL models. If VisCodex were evaluated against them on MCD or InfiBench-V, would the SOTA claim still hold, particularly on chart-to-code or vision-augmented code QA tasks?\n2. Robustness and Failure Analysis\n The paper reports average scores but lacks robustness evaluation and failure case studies.\nHow does VisCodex perform under realistic noisy inputs (e.g., blurred or low-resolution images, ambiguous textual prompts)?\nAfter merging the code vector, does the model retain visual understanding performance (e.g., VQA, captioning), or does catastrophic forgetting occur?\n3. Dataset Construction Details\n For the GPT-4o-generated HTML samples in MCD, what specific prompt template was used?\nDid the prompt enforce particular coding conventions (CSS frameworks, layout constraints)?\nWas there validation (e.g., sampling real-world HTML from open-source sites) to ensure diversity and authenticity?\n4. Task Vector Interactions\n Did you analyze potential bias accumulation when summing task vectors?\nFor example, visualizing semantic directions of τ_vlm and τ_code in parameter space, or correlating changes in λ with performance shifts on specific benchmarks.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. Clear and practically relevant problem setting\nThe paper focuses on enabling multimodal models to generate functional code from visual inputs, a capability that is highly useful in real scenarios such as UI-to-code and chart reconstruction.\n\n2. Simple and effective model fusion design\nThe task-vector–based fusion method offers a lightweight way to combine visual and coding capabilities without full retraining, and ablations show consistent gains across model scales.\n\n3. Constructed datasets and benchmarks with solid empirical utility\nThe MCD dataset and InfiBench-V provide diverse, visually grounded coding tasks, and human evaluation supports the reliability of the benchmark.\n\n4. Competitive performance among open models\nVisCodex surpasses open-source baselines and achieves results close to proprietary systems on visually grounded coding tasks.", "weaknesses": "| 1. Unclear source of improvement and limited evidence of true multimodal fusion\n\nAlthough the model reports strong multimodal code generation results, the source of gains is unclear. The merged model uses CodeQwen2.5-7B as the code branch but does not report its standalone performance on the same benchmarks, making it impossible to separate the benefits of “vision–code unification” from the inherent strength of the code model. Moreover, since both Qwen2.5-VL and CodeQwen2.5 share the same architecture, it is uncertain whether improvements stem from genuine multimodal fusion or simply architectural compatibility, weakening the causal claim of the paper.\n\n| 2. Task vector representation may accumulate bias and lacks theoretical justification\n\nThe method assumes that the difference between a fine-tuned model and its base model cleanly represents a task, but such vectors also contain optimization noise and undesirable parameter shifts. When summing multiple task vectors—especially across heterogeneous domains like vision-language and code—these biases may accumulate and distort semantics. The paper provides no analysis of such interactions, and the balancing factor λ is chosen by grid search rather than learned, suggesting the method may rely on heuristic tuning rather than a principled understanding of knowledge composition in parameter space.\n\n| 3. Dataset and Benchmark Biases Undermine Generalizability\n\nBoth the self-constructed MCD dataset and the InfiBench-V benchmark introduce potential biases that reduce the reliability and generalizability of the reported results.\n\nMCD Dataset\nThe chart image–code subset contains 164k synthetic samples (from ChartCoder) and only 46k real GitHub samples, yet the paper does not quantify the distribution gap in coding style or chart complexity. Heavy reliance on synthetic samples risks overfitting to artificial patterns and weakens real-world chart-to-code applicability.\n\nThe HTML code subset is generated by GPT-4o from webpage screenshots, but the paper does not disclose the prompt template. If the prompt implicitly enforces specific coding conventions (e.g., fixed CSS frameworks), the resulting code may lack diversity and fail to reflect real UI development practices, where developers rely on heterogeneous toolchains (Tailwind, Bootstrap, custom CSS).\n\nInfiBench-V Benchmark\nThe benchmark only retains StackOverflow questions with accepted answers and filters out text-only cases. However, the paper does not analyze whether the remaining 322 samples include difficult real-world cases such as low-resolution screenshots or ambiguous visual-text inconsistencies. This narrow curation limits the benchmark’s ability to evaluate robustness in realistic multimodal coding scenarios.", "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)