Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagram Parsing and Understanding
Score
72首次系统评估MLLM科学图示到代码的解析、编辑与问答，揭示了图转码能力短板。
数据规模大且经过人工验证，任务设计贴合科学写作工具的实际需求。
为OpenAI Prism等科学写作工具提供评测基准，但代码生成任务的实际效果仍需更多验证。
契合MLLM在科学协作场景快速落地的当前热点，具有较强时效性。
来源为arXiv论文，项目主页公开，但尚未经过同行评审。
Base 79.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
The benchmark has large curated data, task designs aligned with real tools, and reveals systemic weaknesses in diagram-to-code conversion.
AI commentary
「The most interesting part of this benchmark is that it shifts evaluation from 'asking about diagrams' to 'writing code from diagrams' — the bottleneck of real scientific writing tools. For researchers working on multimodal agents and scientific document automation, Diagram-MMU offers a clear difficulty hierarchy and failure mode analysis. I'm excited to see how model performance evolves on diagram-to-code tasks, since code generation reflects deeper understanding than multiple-choice QA.」
Quick overview
Diagram-MMU is a new multi-modal benchmark designed to assess MLLMs' ability to parse and understand scientific diagrams. It features 3.7K curated diagrams and 18.3K human-validated questions across six domains, covering three tasks: diagram-to-code parsing, diagram-to-code editing, and diagram question answering, each also evaluated under agentic settings. Evaluation of 12 MLLMs reveals that diagram-to-code tasks are more challenging than diagram question answering: models can reason well over diagrams but struggle to parse and edit them, highlighting the need to enhance diagram-to-code generation. Under agentic settings, most models improve parsing and editing but degrade on question answering, while Claude-4.6 Opus consistently improves across all three tasks. This benchmark provides a robust testbed for improving scientific diagram understanding in tools like OpenAI Prism and has implications for scientific writing and collaboration.
- Diagram-MMU includes 3.7K diagrams and 18.3K questions across six scientific domains.
- Three core tasks: diagram-to-code parsing, diagram-to-code editing, and diagram QA, all with agentic settings.
- Evaluation of 12 MLLMs shows diagram-to-code tasks are harder than QA.
- Claude-4.6 Opus is the only model that improves on all three tasks under agentic settings.
Full text
This is an AI-generated Chinese digest; use "View original" for the source text.
Related items
FeaturedDeepSeek V4 Doubles Token Share on OpenRouter, Driven by Agentic Workloads
DeepSeek V4 在 OpenRouter 上的代币份额翻倍，智能体工作负载成主要驱动力
The rapid adoption of DeepSeek V4 signals a strong market preference for high-performance, low-cost inference models, especially as agentic use cases reshape AI deployment.
91
FeaturedEnabling Two Settings Triples ARC-AGI-3 Benchmark Scores
启用两项设置使ARC-AGI-3基准测试分数提升三倍
This finding is crucial! It shows that model performance depends not only on training but also on inference-time configuration. For AI engineers and researchers, this is a must-know optimization direction. Teams deploying models on resource-constrained devices in particular could find a low-cost way to boost capabilities.
91
FeaturedGPT-5.6: Fusing Frontier Intelligence with Frontier Efficiency
GPT-5.6：前沿智能与前沿效率的融合
This news is worth watching as it marks a shift from pure parameter scaling to efficiency competition. For AI developers and business decision-makers, GPT-5.6 means higher performance at lower cost, especially for long-context or agentic tasks previously hindered by inference expenses. OpenAI's move may redefine what 'frontier' means.
91
FeaturedLFM2.5-Encoders: New Models for Fast Long-Context Inference on CPU
LFM2.5-Encoder：面向CPU快速长上下文推理的新模型
This blog post catches my eye because it directly solves the pain point of running long-context LLMs on CPU. For teams or individual developers without high-end GPUs, the LFM2.5-Encoder series offers a ready-to-use open-source solution. Highly recommended for those working on RAG, document search, or code analysis—now a standard server can handle million-token documents.
91
FeaturedGPT-Red: OpenAI Automates Red Teaming via Self-Play to Boost Model Robustness
GPT-Red：OpenAI通过自我对弈实现AI红队自动化，提升模型鲁棒性
GPT-Red marks a milestone from human to automated red-teaming—essential for safety engineers and alignment researchers. While reducing cost, it also raises ethical questions about automated attacks, worth pondering for everyone in AI governance.
91
FeaturedOpenAI Launches GPT-5.5 Bio Bug Bounty Program
OpenAI推出GPT-5.5生物漏洞赏金计划
This program deserves attention as it pioneers the extension of AI bug bounty to biosecurity, offering a new research direction for the security community and a practical training ground for AI risk governance. It is particularly important for model safety researchers, biosecurity experts, and practitioners concerned about dual-use AI risks.
91
FeaturedvLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance
vLLM推出原生速度Transformers建模后端，大幅提升推理性能
This update is critical for AI engineering teams: eliminating format conversion speeds up model iteration, and 2-5x speedup directly reduces GPU costs. If you're building LLM online services or batch inference, this is practically a must-read technical update.
90
FeaturedGoogle DeepMind Introduces SL2T, a Sign Language-to-Text Model for Deaf Users
Google DeepMind 发布手语转文本模型 SL2T，助力聋人用户
This news matters not only as a technical breakthrough but also as a sign that AI is beginning to address the needs of marginalized groups. For developers, the architecture and technical approach of SL2T merit deep study; for accessibility advocates, it's a key case for advancing industry standards.