TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference

arXiv:2606.27161 · cs.AI · Submitted 2026-06-25 · Read on arXiv

cs.AI

Submitted: 2026-06-25

Updated: 2026-09-23

Code: https://github.com/haotian-liu/LLaVAhttps:

Terminology

• Pruning — 109× in this paper · explained in 4 episodes
• Benchmark — 45× in this paper · explained in 2 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Accuracy — 7× in this paper · explained in 1 episode
• Hallucination — 7× in this paper · explained in 3 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Instruction Tuning — 4× in this paper · explained in 2 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Multimodal Reasoning — 3× in this paper · explained in 5 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Positional Bias — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models
• Qwen Technical Report
• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
• Token Merging: Your ViT But Faster
• GPT-4o System Card
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMs
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
• The Llama 3 Herd of Models
• VideoPoet: A Large Language Model for Zero-Shot Video Generation
• LLaVA-OneVision: Easy Visual Task Transfer
• Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
• Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model
• Instruction Tuning with GPT-4
• OpenAI GPT-5 System Card
• IDPruner: Harmonizing Importance and Diversity in Visual Token Pruning for MLLMs

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