Positional Preservation Embedding for Multimodal Large Language Models

• URL: http://arxiv.org/abs/2510.22936v1
• Date: Mon, 27 Oct 2025 02:40:02 GMT
• Title: Positional Preservation Embedding for Multimodal Large Language Models
• Authors: Mouxiao Huang, Borui Jiang, Dehua Zheng, Hailin Hu, Kai Han, Xinghao Chen,
• Abstract summary: Multimodal language models (LMLMs) have achieved strong performance on vision-language tasks, yet often suffer from inefficiencies due to redundant visual tokens.<n>In this work, we propose a novel encoding novel spatial preservation structure during token compression.<n>We show that PPE can effectively support clustering -- a progressive token compression strategy that leads to better performance retention.
• Score: 20.307929204794917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks, yet often suffer from inefficiencies due to redundant visual tokens. Existing token merging methods reduce sequence length but frequently disrupt spatial layouts and temporal continuity by disregarding positional relationships. In this work, we propose a novel encoding operator dubbed as \textbf{P}ositional \textbf{P}reservation \textbf{E}mbedding (\textbf{PPE}), which has the main hallmark of preservation of spatiotemporal structure during visual token compression. PPE explicitly introduces the disentangled encoding of 3D positions in the token dimension, enabling each compressed token to encapsulate different positions from multiple original tokens. Furthermore, we show that PPE can effectively support cascade clustering -- a progressive token compression strategy that leads to better performance retention. PPE is a parameter-free and generic operator that can be seamlessly integrated into existing token merging methods without any adjustments. Applied to state-of-the-art token merging framework, PPE achieves consistent improvements of $2\%\sim5\%$ across multiple vision-language benchmarks, including MMBench (general vision understanding), TextVQA (layout understanding) and VideoMME (temporal understanding). These results demonstrate that preserving positional cues is critical for efficient and effective MLLM reasoning.

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