arXiv:2507.00037v2 Announce Type: replace Abstract: Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representational divergence arising from permutation invariance, random initialization, and heterogeneous training data. Existing methods struggle particularly in zero-shot settings under non-IID data distributions, and are often limited to specific architectures or pairwise fusion. We introduce a neuron-centric family of fusion algorithms that frames fusion as a principled representation-matching problem: intermed
The paper addresses the persistent challenge of combining independent AI models without extensive retraining, a crucial step for efficient AI deployment and evolution as the field matures.
This development could significantly reduce the computational and data overhead for training new, more robust AI models, accelerating AI development and integration into complex systems.
The ability to fuse models 'zero-shot' across diverse architectures and non-IID data distributions reduces barriers to creating more powerful and specialized AI systems, even from disparate sources.
- · AI developers
- · Cloud AI providers
- · SaaS companies leveraging AI
- · Organizations with heterogeneous AI models
- · AI model retraining services (potentially reduced need)
Easier and faster integration of specialized AI models into larger, more general systems.
Increased modularity and composability of AI systems, leading to more resilient and adaptable AI applications.
Acceleration of AI agent development and deployment by enabling more efficient combination of diverse AI capabilities.
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Read at arXiv cs.LG