TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference
arXiv:2606.27161v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of visual tokens, which introduces substantial computational overhead. Visual token pruning offers a natural solution, yet existing methods are imperfect: attention-based criteria tend to retain redundant tokens, while diversity-based criteria are often agnostic to user instructions. Even methods that combine multiple criteria still lack a principled formulation of the intrinsic objective of token pr
The rapid development and adoption of MLLMs create an urgent need for increased efficiency, driving innovation in areas like token pruning.
This research addresses a fundamental limitation in the efficiency of large multimodal models, which is crucial for their scalability, wider deployment, and reduced computational costs.
The proposed 'TOPS' method offers a more principled approach to visual token pruning, potentially leading to more efficient and adaptable MLLM inference compared to existing ad-hoc solutions.
- · AI developers
- · Cloud providers
- · Companies deploying MLLMs
- · Users of multimodal AI applications
- · Inefficient MLLM architectures
- · Hardware providers unprepared for optimized AI workloads
MLLMs will become significantly more efficient, reducing inference costs and latency.
This efficiency gain will enable the deployment of more complex and higher-fidelity multimodal AI applications across various sectors.
Reduced compute demands could ease pressure on the compute supply chain and energy grids, contributing to more sustainable large-scale AI development.
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Read at arXiv cs.AI