USAI-Quant: A Quantitative Reasoning Benchmark for Vision-Language Models in Built Environments

arXiv:2609.32813 · cs.CV, cs.CL, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.CL, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Benchmark — 32× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Visual Question Answering — 7× in this paper
• Foundation Model — 5× in this paper · explained in 3 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Vision-language models — 5× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Instruction Tuning — 4× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes

Sources

• Qwen Technical Report
• Qwen3-VL Technical Report
• An Introduction to Vision-Language Modeling
• LLaMA: Open and Efficient Foundation Language Models

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