InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation

InteractScience is a benchmark specifically designed to evaluate the capability of large language models in generating interactive scientific demonstration code. This project provides a complete evaluation pipeline including model inference, automated testing, and multi-dimensional assessment.

📁 Directory Structure

.
├── data/ # Benchmark dataset
│ ├── interactscience.jsonl # Main dataset file containing problems and references
│ └── snapshots/ # Reference screenshot directory
│ ├── *_Snapshot-1.png
│ ├── *_Snapshot-2.png
│ └── ...
├── PFT_tests/ # Program Functionality Testing (PFT) scripts
│ ├── *.spec.js # Playwright test scripts
│ └── ...
├── VQT_tests/ # Visual Quality Testing (VQT) scripts
│ ├── *.spec.js # Playwright test scripts
│ └── ...
├── eval/ # Model inference results
│ ├── interactscience_lm_*.jsonl # Language model inference results
│ ├── interactscience_vlm_*.jsonl # Vision-language model inference results
│ └── ...
├── results/ # Test result data
│ ├── lm_results/ # Language model test results
│ │ ├── PFT_test_results/ # Program functionality test results
│ │ ├── VQT_test_results/ # Visual quality test results
│ │ ├── VQT_clip_results/ # CLIP scoring results
│ │ └── VQT_vlm_judge_results/ # VLM scoring results
│ └── vlm_results/ # Vision-language model test results
├── run_generation.sh # Model inference script
├── run_benchmark.sh # Automated testing script
├── run_vlm_as_judge.sh # VLM scoring script
├── cal_metrics.py # Metrics calculation script
├── test_llm.py # Language model testing main program
├── vlm_as_judge.py # VLM scoring main program
├── clip_score.py # CLIP score calculation
└── extract_and_save_code.py # Code extraction and saving

🚀 Usage Tutorial

1. Environment Setup

First install Node.js and npm, then install the Playwright testing environment:

# Install project dependencies
npm install
# Install Playwright browsers
npx playwright install

2. Model Inference

Use the run_generation.sh script for model inference:

# Edit the model path and parameters in the script
vim run_generation.sh
# Run inference (requires model path configuration)
bash run_generation.sh

Script Description:

• Starts vLLM API server
• Calls test_llm.py for inference
• Results saved to eval/ directory

3. Automated Testing

Use the run_benchmark.sh script for automated testing:

# Set the model name to testexport MODEL="your_model_name"# Run tests
bash run_benchmark.sh

Testing Process:

1. Extract HTML code from inference results (extract_and_save_code.py)
2. Execute Program Functionality Testing (PFT) using playwright_PFT.config.js
3. Execute Visual Quality Testing (VQT) using playwright_VQT.config.js
4. Calculate CLIP similarity scores (clip_score.py)
5. Results saved to results/ directory

4. VLM Scoring

Use run_vlm_as_judge.sh for VLM-as-Judge evaluation:

# Edit model and path configuration in the script
vim run_vlm_as_judge.sh
# Run VLM scoring
bash run_vlm_as_judge.sh

Scoring Description:

• Uses vision-language models to score generated results
• Compares reference screenshots with generated screenshots
• Evaluation based on predefined checklists

5. Results Analysis

Use cal_metrics.py and cal_vlm_as_judege_score.py to calculate final metrics:

python cal_metrics.py
python cal_vlm_as_judege_score.py

📊 Dataset Description

interactscience.jsonl

Main dataset file, each line contains a test sample:

• id: Unique identifier
• question: Detailed HTML implementation plan
• lm_system_prompt: Language model system prompt
• vlm_system_prompt: Vision-language model system prompt
• image_path: List of reference screenshot paths
• snapshot_checklists: Visual verification checklists

Reference Screenshots

Located in data/snapshots/ directory, naming format:

• {task_id}_Snapshot-{number}.png

🧪 Test Types

1. Program Functionality Testing (PFT)

• Validates functional correctness of HTML code
• Checks interactive element behavior
• Tests JavaScript logic

2. Visual Quality Testing (VQT)

• Generates page screenshots
• Compares with reference screenshots
• Calculates perceptual similarity (CLIP scores)
• Calculates semantic correctness (VLM-judge scores)

🛠️ Core Scripts Description

test_llm.py

Language model testing main program:

python test_llm.py \
--dataset_path data/interactscience.jsonl \
--prompt_type lm_system_prompt \
--dump_path eval/result.jsonl \
--model_path your_model_path \
--base_url http://localhost:8000/v1 \
--api_key EMPTY

vlm_as_judge.py

VLM scoring main program:

python vlm_as_judge.py \
--reference_image_dir data/snapshots \
--generated_image_dir generated_images \
--checklist_file data/checklists.jsonl \
--output_path results/vlm_judge.jsonl \
--base_url your_api_endpoint \
--api_key your_api_key

📈 Evaluation Metrics

• Program Functionality Test Pass Rate: Percentage of PFT test cases passed
• Visual Quality Score: Visual similarity based on CLIP model
• VLM Score: Comprehensive score given by multimodal models

Experiments

We have evaluated 30 state-of-the-art large language models on the InteractScience benchmark. The results are available in the results/ directory.

Model	PFT Overall (%)	PFT Average (%)	PFT Perfect (%)	VQT Action (%)	VQT CLIP	VQT VLM-judge
Closed-Source Large Language Models
GPT-5	39.47	37.61	16.08	89.66	71.95	57.02
GPT-4.1	37.07	34.08	11.19	89.15	71.21	52.84
GPT-4o	28.27	27.09	5.59	85.93	67.11	42.45
o3	34.93	32.09	13.99	89.83	72.24	52.82
o4-mini	37.33	34.90	13.29	88.64	71.79	51.90
Gemini-2.5-Pro	35.33	34.62	11.19	86.78	70.65	54.69
Gemini-2.5-Flash	31.60	31.07	10.49	86.95	69.59	49.34
Claude-Sonnet-4-20250514	41.47	37.40	13.29	89.66	73.50	55.42
Claude-Opus-4-20250514	40.27	36.34	11.19	89.32	73.22	54.93
Claude-3.5-Sonnet	33.33	31.45	9.79	90.17	72.32	49.43
Open-Source Large Language Models
DeepSeek-R1-0528	33.87	32.02	8.39	88.31	69.54	49.46
DeepSeek-V3-0324	31.73	30.57	10.49	85.93	68.68	49.46
Kimi-K2	31.60	31.22	9.79	87.29	70.11	50.04
GLM-4.5	29.33	26.65	8.39	70.51	55.90	38.57
Intern-S1	31.87	28.93	7.69	87.46	68.74	45.27
gpt-oss-120b	28.00	27.78	9.79	90.85	72.13	49.57
gpt-oss-20b	15.20	12.97	3.50	80.51	54.68	21.40
Qwen3-235B-A22B-Instruct-2507	33.33	31.46	13.29	78.14	70.02	45.14
Qwen3-32B	27.20	24.09	5.59	87.46	66.46	39.69
Qwen3-14B	24.13	23.58	7.69	85.08	66.46	36.53
Qwen3-8B	20.00	18.85	4.20	81.53	64.13	34.67
Qwen3-4B	14.67	13.10	2.80	82.03	60.90	28.33
Qwen3-1.7B	6.53	6.22	1.40	75.76	59.65	20.33
Qwen2.5-Coder-32B-Instruct	27.20	25.10	7.69	84.58	51.67	38.51
Qwen2.5-Coder-14B-Instruct	22.53	20.61	4.90	85.42	64.47	35.72
Qwen2.5-Coder-7B-Instruct	12.40	10.51	0.70	82.37	65.17	26.97
Qwen2.5-VL-72B-Instruct	23.73	22.82	6.99	87.12	64.33	37.30
Qwen2.5-VL-7B-Instruct	7.47	6.72	0.70	70.00	49.49	20.41
Llama-3.1-70B-Instruct	18.67	18.04	4.90	88.64	59.56	33.36
Llama-3.1-8B-Instruct	11.33	10.16	3.50	80.00	65.42	22.75

Comparison Across Difficulty Levels

Comparison Across Disciplines

Results on Multimodal LLMs with Reference Snapshots as Input

Example Cases

Citation

@article{InteractScience,
author = {Qiaosheng Chen and Yang Liu and Lei Li and Kai Chen and Qipeng Guo and Gong Cheng and Fei Yuan},
title = {InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation}, journal = {arXiv preprint arXiv:2510.09724},
year = {2025}
}