## 1 Introduction  
Restricted and Repetitive Behaviors (RRBs) are a characteristic trait of children with autism spectrum disorder (ASD) [1], typically manifesting as an excessive adherence to specific behavioral patterns and repetitive actions, often lacking purpose and flexibility [2]. The negative impact of RRBs on children with autism extends to areas such as social integration and cognitive learning. Additionally, dangerous behaviors such as self-harm are also categorized as RRBs [3, 4], potentially posing risks to the safety and well-being of the child. Therefore, timely assessment and intervention of RRBs are critical for improving the quality of life of affected individuals. Historically, the assessment of RRBs relied largely on standardized scales. While this method provides a structured approach, it often introduces subjectivity [5], particularly when input is derived from parents, who may have inherent biases [6]. In addition, RRBs demonstrate significant variation throughout a child's developmental trajectory, necessitating long-term monitoring and evaluation of symptoms [7]. Multiple factors, including insufficient knowledge about ASD among healthcare providers, parental neglect of early signs, and the long-term nature of symptom tracking, complicate the early diagnosis and intervention of RRBs [8; 9]. Thus, there is an urgent need for a robust and precise automated perception method to identify RRBs in children with ASD. Artificial intelligence has become increasingly prevalent in this field due to its low cost, reliability, and accuracy, significantly advancing the automatic assessment of RRBs. However, existing studies on the automatic identification of ASD-related behaviors still face limitations. Firstly, traditional machine learning approaches rely significantly on extensive feature engineering and often struggle with complex behavioral patterns and high-dimensional data [10], resulting in suboptimal identification accuracy, such as the mere 47.1% achieved in the study by [PERSON] et al. [11]. The video transformer [12], leveraging self-attention mechanisms, is more effective in capturing long-range spatiotemporal dependencies within video sequences, achieving over a 10% performance improvement on public datasets related to RRBs [13]. Notably, effective training of video transformers typically requires large-scale datasets, exemplified by the widely used Kinetics-400, which comprises over 200,000 video segments. In contrast, the smaller scales of existing ASD behavioral datasets may lead to model overfitting [14], undermining practical application. Multi-modal large language models (MLLMs) exhibit impressive capabilities in visual-language understanding, potentially addressing the gap in interpretable reasoning within the field of ASD behavior analysis. However, their performance in zero-shot recognition remains challenging. For instance, [PERSON] et al. found that both GPT-4V and LLaVa achieved recognition accuracies below 20% on the newly proposed AV-ASD dataset, underperforming compared to vision-based models [15]. Furthermore, training or fine-tuning large parameter-scale MLLMs often requires substantial computational resources [16], making the efficient application of MLLM for ASD behavior analysis a pressing issue. Current solutions being explored include model compression techniques to alleviate computational burdens [17], and transfer learning approaches that involve pre-training models on large-scale general datasets before transfer to ASD behavior recognition tasks to enhance zero-shot recognition accuracy while reducing computational costs [13]. To address these challenges, this study proposes the MS-RRBR, a multi-model synergetic framework for the automatic recognition of RRBs. Our approach effectively integrates the strengths of behavior recognition models with MLLM, aiming to enhance the accuracy of RRB identification while tackling the issues of insufficient data scale and computational resource consumption.