OmniPhys: A Unified Multimodal Benchmark for Physics Understanding and Generation from Chinese Educational Corpora

arXiv:2608.25398 · cs.CL · Submitted 2026-08-26 · Read on arXiv

cs.CL

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/ECNU-RAIL/OmniPhys-EMNLP2026

Terminology

• Benchmark — 43× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Ablation — 7× in this paper · explained in 1 episode
• Accuracy — 7× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Core — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 4× in this paper · explained in 3 episodes
• Multimodal Reasoning — 4× in this paper · explained in 5 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment Scores — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Cognitive Load — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Phi-4 Technical Report
• LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
• Qwen3-VL Technical Report
• Hallucination of Multimodal Large Language Models: A Survey
• InternLM2 Technical Report
• TheoremQA: A Theorem-driven Question Answering dataset
• PhysicsArena: The First Multimodal Physics Reasoning Benchmark Exploring Variable, Process, and Solution Dimensions
• Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level
• The Llama 3 Herd of Models
• RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs
• Measuring Massive Multitask Language Understanding
• OpenAI o1 System Card
• Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents
• K12Vista: Exploring the Boundaries of MLLMs in K-12 Education
• DeepSeek-V3 Technical Report
• DeepSeek-VL: Towards Real-World Vision-Language Understanding
• MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
• Multi-Physics: A Comprehensive Benchmark for Multimodal LLMs Reasoning on Chinese Multi-Subject Physics Problems
• ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
• MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

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