qvq-plus-latest

QVQ is an experimental multimodal reasoning model launched by Alibaba’s Tongyi Lab. Its core mission is to serve as an open-source visual-language research foundation that delivers “stronger visual understanding + deep and complex reasoning.”

• Outstanding Visual Reasoning Capabilities: Built on Qwen2-VL-72B, it significantly outperforms the original model in challenging multimodal benchmarks such as MMMU, MathVista, MathVision, and OlympiadBench, particularly excelling in mathematics and science domains.
• Step-by-Step Solving of Complex Tasks: It supports detailed, step-by-step reasoning, enabling logical decomposition and rigorous derivation for physics problems, geometric diagrams, academic charts, and more.
• Comprehensive Multidisciplinary Understanding: It covers university-level content in mathematics, physics, chemistry, biology, and other disciplines, and can handle problems at the level of Olympic competitions as well as authentic questions from China’s National College Entrance Examination.

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Core Capabilities

👁️ Advanced Visual Analysis: Accurately interprets specialized visual content such as function graphs, circuit diagrams, molecular structures, and geometric proof diagrams.

🧠 Deep Cross-Modal Reasoning: Combines image information with symbolic logic to tackle complex tasks like “finding derivatives from tables” or “inferring experimental conclusions from paper illustrations.”

🧮 Solution Skills at the Level of Mathematical Competitions: Demonstrates solution approaches and accuracy comparable to human competitors when tackling real mathematical competition problems (e.g., MathVision).

🔍 Expert-Level Annotation Alignment: Trained using expert step-by-step reasoning annotations from OlympiadBench, ensuring that the output process aligns closely with teaching and research standards.