## 3 Methods  
### Synergistic Workflow of MS-RRBR  
MS-RRBR, proposed in this study, is a multi-model synergistic framework designed for recognizing RRBs in children with autism. The framework implements an end-to-end processing pipeline, from video input to behavior recognition output, through a systematic module design and an innovative collaborative mechanism. Figure 1 illustrates the workflow of MS-RRBR, which consists of four stages: category candidate generation, visual question answering (VQA), fact verification, and inference decision-making. GPT-4o serves as the core component of MS-RRBR, orchestrating the interaction between various behavior recognition models, including UniformeV2 for behavior recognition, InternVL for multimodal understanding, and Grounding Dino for open-set object detection, ultimately performing the collaborative decision-making process. This staged processing approach not only ensures high-quality handling at each step but also provides a traceable evidence chain for subsequent result interpretation.  
In the category candidate generation stage, UniformeV2 is used to perform a preliminary classification of behavior types. We employ a transfer learning strategy to retrain UniformeV2. After preprocessing, video input is fed into Uniform  
Figure 1: The synergistic workflow of the MS-RRBR framework consists of four stages: category candidate generation, visual question answering (VQA), fact verification, and inference decision-making. This process utilizes the UniformeV2 model for preliminary behavior classification, combined with GPT-4o and Grounding Dino for multi-round visual question answering and fact verification, enhancing the recognition accuracy of RRBs.  
dictionary containing the predicted labels and the corresponding probabilities. To improve behavior recognition accuracy in subsequent steps, we select the top five predicted results, representing these as candidate labels for the VQA stage, given that the Top-5 accuracy generally improves significantly compared to the Top-1.  
In the VQA stage, GPT-4o generates guiding questions about candidate categories to assist MLLM in recognizing behaviors more accurately. Studies indicate that the visual encoder of MLLM often overlooks critical visual details in images [40]. Therefore, instead of asking direct visual questions like \"What kind of repetitive behavior is exhibited by the child?\", we employ GPT-4o to generate a series of descriptive questions to guide MLLM toward a finer-grained behavior analysis.  
In the fact verification stage, GPT-4o collaborates with Grounding Dino to verify if the visual answers align with factual details. GPT-4o extracts key objects related to the behavior from the answer's textual description, while Grounding Dino detects these objects' presence and locations within the given image, helping identify any \"hallucinated\" responses from MLLM.  
Finally, in the inference decision-making stage, GPT-4o consolidates visual information from the outputs of all previous models to generate a final behavior classification decision. In addition to the category prediction, GPT-4o provides an explanatory text detailing the reasoning process and verification basis, ensuring the interpretability and validity of the prediction.