LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917085271031808 |
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| author | Lv, Chengtao Zhang, Bilang Yong, Yang Gong, Ruihao Huang, Yushi Gu, Shiqiao Wu, Jiajun Shi, Yumeng Guo, Jinyang Wang, Wenya |
| author_facet | Lv, Chengtao Zhang, Bilang Yong, Yang Gong, Ruihao Huang, Yushi Gu, Shiqiao Wu, Jiajun Shi, Yumeng Guo, Jinyang Wang, Wenya |
| contents | Large Vision-Language Models (VLMs) exhibit impressive multi-modal capabilities but suffer from prohibitive computational and memory demands, due to their long visual token sequences and massive parameter sizes. To address these issues, recent works have proposed training-free compression methods. However, existing efforts often suffer from three major limitations: (1) Current approaches do not decompose techniques into comparable modules, hindering fair evaluation across spatial and temporal redundancy. (2) Evaluation confined to simple single-turn tasks, failing to reflect performance in realistic scenarios. (3) Isolated use of individual compression techniques, without exploring their joint potential. To overcome these gaps, we introduce LLMC+, a comprehensive VLM compression benchmark with a versatile, plug-and-play toolkit. LLMC+ supports over 20 algorithms across five representative VLM families and enables systematic study of token-level and model-level compression. Our benchmark reveals that: (1) Spatial and temporal redundancies demand distinct technical strategies. (2) Token reduction methods degrade significantly in multi-turn dialogue and detail-sensitive tasks. (3) Combining token and model compression achieves extreme compression with minimal performance loss. We believe LLMC+ will facilitate fair evaluation and inspire future research in efficient VLM. Our code is available at https://github.com/ModelTC/LightCompress. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_09981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit Lv, Chengtao Zhang, Bilang Yong, Yang Gong, Ruihao Huang, Yushi Gu, Shiqiao Wu, Jiajun Shi, Yumeng Guo, Jinyang Wang, Wenya Computer Vision and Pattern Recognition Large Vision-Language Models (VLMs) exhibit impressive multi-modal capabilities but suffer from prohibitive computational and memory demands, due to their long visual token sequences and massive parameter sizes. To address these issues, recent works have proposed training-free compression methods. However, existing efforts often suffer from three major limitations: (1) Current approaches do not decompose techniques into comparable modules, hindering fair evaluation across spatial and temporal redundancy. (2) Evaluation confined to simple single-turn tasks, failing to reflect performance in realistic scenarios. (3) Isolated use of individual compression techniques, without exploring their joint potential. To overcome these gaps, we introduce LLMC+, a comprehensive VLM compression benchmark with a versatile, plug-and-play toolkit. LLMC+ supports over 20 algorithms across five representative VLM families and enables systematic study of token-level and model-level compression. Our benchmark reveals that: (1) Spatial and temporal redundancies demand distinct technical strategies. (2) Token reduction methods degrade significantly in multi-turn dialogue and detail-sensitive tasks. (3) Combining token and model compression achieves extreme compression with minimal performance loss. We believe LLMC+ will facilitate fair evaluation and inspire future research in efficient VLM. Our code is available at https://github.com/ModelTC/LightCompress. |
| title | LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.09981 |