# simple-evals-mm: A Multimodal Evaluation Framework for Vision-Language Models, Facilitating Standardization of VLM Performance Assessment

> This introduces the simple-evals-mm project developed by the llm-jp team, an OpenAI simple-evals-based extended multimodal evaluation framework that supports over 20 benchmark tests, covering authoritative datasets like AI2D, MMMU, and ScienceQA, providing a standardized evaluation solution for Vision-Language Models.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-06T00:44:15.000Z
- 最近活动: 2026-04-06T00:50:12.772Z
- 热度: 154.9
- 关键词: 视觉语言模型, VLM评测, 多模态AI, 基准测试, JAMMEval, OpenAI, Gemini, Qwen-VL, AI评估, 开源框架
- 页面链接: https://www.zingnex.cn/en/forum/thread/simple-evals-mm-vlm
- Canonical: https://www.zingnex.cn/forum/thread/simple-evals-mm-vlm
- Markdown 来源: floors_fallback

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## simple-evals-mm: Guide to the Standardized Multimodal Evaluation Framework for Vision-Language Models

simple-evals-mm is an open-source project developed by the llm-jp team, extended from OpenAI simple-evals, specifically designed to provide a standardized evaluation solution for Vision-Language Models (VLMs). This framework supports over 20 authoritative benchmark tests, covering multimodal datasets such as AI2D, MMMU, and ScienceQA. It is also an important component of the JAMMEval evaluation project, aiming to address the lack of objectivity and comprehensiveness in VLM assessments.

## Project Background: Evaluation Challenges Amid Rapid VLM Development

With the rapid development of VLMs like GPT-4V, Gemini, and Qwen-VL, traditional text model evaluation frameworks can no longer meet the needs of multimodal evaluation, as existing tools lack uniformity and scalability. Against this backdrop, the llm-jp team launched simple-evals-mm as a multimodal extended version of OpenAI simple-evals, providing systematic support for VLM performance evaluation.

## Core Features: Coverage of Multidimensional and Multilingual Evaluation Capabilities

### Multimodal Benchmark Datasets
Integrates over 20 authoritative English datasets such as ChartQA (Chart Question Answering), AI2D (Scientific Diagram Understanding), and MMMU (Multidisciplinary Multimodal Understanding), covering dimensions like chart/document comprehension, scientific reasoning, fine-grained recognition, and real-world scenarios.
### Japanese Scenario Support
Integrates Japanese benchmarks from the JAMMEval series such as CC-OCR, JDocQA, and JMMMU, filling the gap in Japanese VLM evaluation.
### Text Capability Preservation
Retains classic text tests like GPQA, MATH, and MMLU to comprehensively assess the model's basic language capabilities.

## Technical Architecture: Flexible Compatibility and Efficient Analysis Tools

### Multi-Backend Model Compatibility
Supports OpenAI (GPT-4o, GPT-5.1), Google Gemini, and open-source models (InternVL, Qwen-VL, etc.), enabling fair comparison of different VLMs.
### Modern Environment Management
Uses uv (a high-speed package manager written in Rust), with `uv sync` for quick environment configuration and `uv run` to execute scripts ensuring consistency.
### Result Analysis Tools
Built-in visualization scripts generate comparison charts; an interactive web viewer supports side-by-side viewing of model outputs and images, facilitating error pattern analysis.

## Usage Guide: Concise Workflow and Structured Result Output

### CLI Tool Workflow
1. List available models: `uv run python src/simple_evals_mm/simple_evals.py --list-models`
2. List evaluation tasks: `uv run python src/simple_evals_mm/simple_evals.py --list-evals`
3. Execute evaluation: Specify the model and benchmark; supports repeated runs to obtain statistical significance.
### Dataset Management
Most benchmarks are automatically downloaded from HuggingFace; preparation guides are provided for special datasets.
### Result Format
Saves three layers of results in JSONL format: single-sample detailed output, aggregated scores, and statistical summaries (mean, standard deviation, etc.).

## Academic Value and Community Contributions: Promoting Standardization and Open Collaboration

The project has published a related paper (arXiv:2604.00909) that elaborates on the JAMMEval benchmark construction concept and evaluation methodology. It is open-sourced under the MIT license and provides CONTRIBUTING.md to guide community contributions. It also points out limitations: the flexibility constraints in model output evaluation may lead to underestimation of strong models' performance, reflecting academic rigor.

## Summary and Outlook: Future Directions for VLM Evaluation Standardization

simple-evals-mm is an important step toward the standardization and systematization of VLM evaluation, providing reliable infrastructure for VLM research and development. In the future, it will further expand coverage of emerging evaluation sets, support more model backends, and continuously innovate evaluation methodologies. It is an open-source project worth attention for professionals in VLM research, development, and application.