# SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMs

arXiv: https://arxiv.org/abs/2510.24214 · HTML: https://mlsanity.com/paper/2510.24214/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Visual token pruning and efficiency, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 42 of its 48 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Visual token pruning and efficiency](https://mlsanity.com/mllm/visual-token-pruning-and-efficiency/)
- Efficient LLM inference and serving / [Visual token pruning in multimodal models](https://mlsanity.com/inference/visual-token-pruning-in-multimodal-models/)

## Builds on (most cited first, 30 of 42)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2308.02490/) (2023)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models](https://mlsanity.com/paper/2403.06764/) (2024)
- [ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](https://mlsanity.com/paper/1906.02467/) (2019)
- [LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models](https://mlsanity.com/paper/2311.17043/) (2023)
- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models](https://mlsanity.com/paper/2403.15388/) (2024)

## Built on by (most cited first, 24 of 24)

- [AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compression](https://mlsanity.com/paper/2602.21233/) (2026)
- [IDPruner: Harmonizing Importance and Diversity in Visual Token Pruning for MLLMs](https://mlsanity.com/paper/2602.13315/) (2026)
- [RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference](https://mlsanity.com/paper/2605.00392/) (2026)
- [QMoP: Query Guided Mixture-of-Projector for Efficient Visual Token Compression](https://mlsanity.com/paper/2603.21232/) (2026)
- [OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning](https://mlsanity.com/paper/2605.29657/) (2026)
- [TRIO: Token Reduction via Inference-Objective Guidance for Efficient Vision-Language Models](https://mlsanity.com/paper/2602.04657/) (2026)
- [TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference](https://mlsanity.com/paper/2606.27161/) (2026)
- [Do Vision Language Models Need to Process Image Tokens?](https://mlsanity.com/paper/2604.09425/) (2026)
- [When Does Visual Token Pruning Improve Calibration? The Role of Evidence Coverage in MLLMs](https://mlsanity.com/paper/2604.12035/) (2026)
- [OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models](https://mlsanity.com/paper/2605.11803/) (2026)
- [DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models](https://mlsanity.com/paper/2608.04496/) (2026)
- [When Vision Becomes Text: Visual Token Pruning via Cross-Modal Residual Guidance in VLMs](https://mlsanity.com/paper/2608.10489/) (2026)
- [STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models](https://mlsanity.com/paper/2609.05916/) (2026)
- [IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models](https://mlsanity.com/paper/2604.00757/) (2026)
- [DCP-Prune: Ultra-Low Token Pruning with Distribution Consistency Preservation](https://mlsanity.com/paper/2606.16633/) (2026)
- [Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference](https://mlsanity.com/paper/2608.00077/) (2026)
- [PACE: A Unified Condense-and-Extract Paradigm for Fast VLM Inference](https://mlsanity.com/paper/2608.27206/) (2026)
- [Focus Where It Counts: A Salience-Driven Vision-Language Model for Low Vision Assistance](https://mlsanity.com/paper/2608.28218/) (2026)
- [TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents](https://mlsanity.com/paper/2609.10297/) (2026)
- [Selective Amortization of Full-Budget Counterfactual Reasoning for Visual Token Communication](https://mlsanity.com/paper/2609.30756/) (2026)
- [CoViST: Visual Token Compression via Composable States](https://mlsanity.com/paper/2609.33397/) (2026)
- [MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference](https://mlsanity.com/paper/2609.34330/) (2026)
- [ACPruner: Visual Token Pruning as Biased Attention Coverage Maximization in LVLMs](https://mlsanity.com/paper/2609.34558/) (2026)
- [ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression](https://mlsanity.com/paper/2609.37225/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) typically process a large number of visual tokens, leading to considerable computational overhead, even though many of these tokens are redundant. Existing visual token pruning methods primarily focus on selecting the most salient tokens based on attention scores, resulting in the semantic incompleteness of the selected tokens. In this paper, we propose a novel visual token pruning strategy, called Saliency-Coverage Oriented token Pruning for Efficient MLLMs (SCOPE), to jointly model both the saliency and coverage of the selected visual tokens to better preserve semantic completeness. Specifically, we introduce a set-coverage for a given set of selected tokens, computed based on the token relationships. We then define a token-coverage gain for each unselected token, quantifying how much additional coverage would be obtained by including it. By integrating the saliency score into the token-coverage gain, we propose our SCOPE score and iteratively select the token with the highest SCOPE score. We conduct extensive experiments on multiple vision-language understanding benchmarks using the LLaVA-1.5 and LLaVA-Next models. Experimental results demonstrate that our method consistently outperforms prior approaches. Our code is available at hrefhttps://github.com/kinredon/SCOPEhttps://github.com/kinredon/SCOPE.