Hierarchical Compression of Vision-Language Model Benchmarks

arXiv:2609.37515 · cs.LG, cs.AI, cs.CL, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.LG, cs.AI, cs.CL, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Benchmark — 223× in this paper · explained in 2 episodes
• Pruning — 31× in this paper · explained in 4 episodes
• Embeddings — 16× in this paper · explained in 2 episodes
• Hallucination — 14× in this paper · explained in 4 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Fidelity — 13× in this paper · explained in 2 episodes
• Hallucination Detection — 11× in this paper · explained in 2 episodes
• Grounding — 9× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• External Validation — 5× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 4 episodes
• Data Pruning — 4× in this paper · explained in 2 episodes
• Vision-language models — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Redundancy — 3× in this paper · explained in 2 episodes
• Hierarchical Framework — 2× in this paper · explained in 2 episodes
• Winter Conference on Applications of Computer Vision — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
• LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• Microsoft COCO Captions: Data Collection and Evaluation Server
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• The Llama 3 Herd of Models
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
• DatBench: Discriminative, Faithful, and Efficient VLM Evaluations
• SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
• Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
• Holistic Evaluation of Language Models
• SmolVLM: Redefining small and efficient multimodal models
• Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
• Gemini Robotics: Bringing AI into the Physical World
• Gemma 3 Technical Report
• Gemma 4 Technical Report
• Kimi-VL Technical Report
• HyperCLOVA X 8B Omni

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