CoLLM: Industrial Large-Small Model Collaboration with Fuzzy Decision-making Agent and Self-Reflection

In industrial applications, large models have exhibited superior generalization capabilities that are unattainable with smaller models. However, when faced with edge scenarios and highly diverse industrial samples, their deployment remains challenging due to high computational costs and unreliable output. To address these challenges, we propose CoLLM, a fuzzy large-small model collaborative framework, which dynamically selects between small and large models based on the characteristics exhibited by the samples. Specifically, this approach estimates uncertainty from input samples to guide model selection: low-uncertainty samples are processed by the small model for efficiency, while high-uncertainty or complex samples are routed to the large model for improved accuracy. It first constructs a fuzzy decision-making agent based on the fuzzy neural network (FNN) to assess sample complexity and determine the appropriate model for inference. Furthermore, a self-reflection mechanism is proposed to refine the large model's output, reducing the risk of unreliable output. Experimental results in industrial time series datasets demonstrate that our framework improves the computational efficiency of large models up to 14.54x while maintaining or improving prediction accuracy.