arXiv:2601.05106v4 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a single general-purpose model typically requires scaling to sizes that are prohibitively expensive to train and deploy. On the other hand, while smaller domain-specialized models are much more efficient, they struggle to generalize beyond their training distributions. To address this dilemma, we propose FusionRoute, a robust and effective token-level multi-LLM collaboration framework in which a lightwei
The increasing cost and complexity of training and deploying large general-purpose LLMs are driving research into more efficient and collaborative architectures.
This research addresses the fundamental challenge of balancing LLM performance, efficiency, and generalization, which is crucial for their broader adoption and industrial application.
The focus is shifting towards efficient multi-model collaboration to achieve strong performance without the prohibitive cost of monolithic, ultra-large language models.
- · AI startups
- · Open-source AI community
- · Enterprises deploying AI
- · Cloud providers offering AI services
- · Companies solely focused on general-purpose monolithic LLM development
- · Compute hardware manufacturers reliant on singular, massive model demand
Reduced computational costs and increased accessibility for advanced AI capabilities.
Democratization of sophisticated AI tools, fostering innovation across a wider array of developers and businesses.
Accelerated development of AI agents capable of specialized, high-performance tasks by combining diverse model strengths.
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Read at arXiv cs.LG