arXiv:2512.01461v2 Announce Type: replace Abstract: Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, traditional basic merging methods often experience performance degradation due to parameter conflicts, even when applied to similar tasks. While recent personalized merging frameworks successfully preserve task-specific information to maintain performance, they typically incur storage overhead. In this paper, we propose Decomposition, Thresholding, and Scaling (DTS), an approximation-based personalized merging framework
The increasing complexity and computational demands of multi-task AI models necessitate more efficient merging techniques to address performance degradation and storage overheads.
This research offers a method to create more efficient and capable multi-task AI models, potentially accelerating AI development and reducing computational resource requirements.
AI models can now integrate multiple functionalities with less performance loss and reduced storage burden, enabling more sophisticated and deployable AI applications.
- · AI developers
- · Cloud providers
- · Edge AI manufacturers
- · Data centers
More versatile and resource-efficient AI models become widely accessible.
Accelerated deployment of complex AI systems across various industries due to reduced operational costs.
Enhanced competition in AI development as smaller entities can leverage multi-task models more effectively.
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