NeurIPS 2025🔬MoB: Near-Lossless Visual Token Pruning with 9× Compression

Congratulations! Professor Xiong’s paper has been accepted to NeurIPS 2025!

Dear colleagues and students,

We are thrilled to announce that the research work by Professors Zhan and Xiong, titled “Why 1+1<1 in Visual Token Pruning: Beyond Naïve Integration via Multi-Objective Balanced Covering,” has been officially accepted to NeurIPS 2025, a premier conference in artificial intelligence!

Addressing the critical challenge of inefficient inference in large vision-language models, this paper provides a deep analysis of the inherent trade-off between two key objectives in visual token pruning: visual fidelity and prompt alignment. For the first time, the authors leverage geometric covering theory to derive a closed-form upper bound on pruning error and quantify the optimal achievable performance for both objectives under a fixed token budget. Building upon this theoretical insight, the team proposes a novel Multi-objective Balanced Covering (MoB) algorithm. MoB reformulates token pruning as a bi-objective covering problem and introduces a greedy radius-trading strategy that transforms the complex multi-objective trade-off into a simple budget allocation problem—enabling significant acceleration while preserving high performance.

Extensive experiments demonstrate MoB’s exceptional effectiveness across multiple state-of-the-art multimodal large models, including LLaVA, Qwen2-VL, and Video-LLaVA:

• On LLaVA-1.5-7B, MoB retains 96.4% of the original performance while keeping only 11.1% of the visual tokens.
• On LLaVA-Next-7B, it achieves 1.3–1.5× speedup with negligible performance degradation.

Moreover, MoB integrates seamlessly into advanced models, showcasing remarkable generality and practicality.