## 5 Discussion
### Interpretability in Inference and Self-Verification  
Traditional machine learning and deep learning models are often regarded as \"black-box\" models due to their lack of transparency. The complexity of these models makes manual calibration difficult and increases the likelihood of misjudgments and erroneous decisions. [PERSON] [47] emphasizes that in high-risk decision-making areas, such as healthcare, interpretable models should be used instead. MS-RRBR is not a black-box classifier; instead, it provides clear reasoning in behavior classification, specific visual detail descriptions, and object-level interaction among models. It uses GPT-4o in collaboration with object detection models for fact verification, filtering out \"hallucinated\" answers to prevent misleading results caused by MLLM's errors. For complex, composite action recognition, although MS-RRBR may sometimes provide incorrect answers in single-behavior decisions, its reasoning explanations also consider factual behaviors as candidate possibilities. Compared to the method proposed by [PERSON] et al. [15], MS-RRBR significantly improves the reliability of decision-making outcomes, offering more comprehensive support and explanations to medical professionals and parents of children with autism in clinical applications.  
Figure 7 illustrates a real case of error correction by the MS-RRBR framework, showcasing the high quality and utility of our method in generating explanations. Initially, UniformV2 incorrectly predicted \"head shaking\" as the most likely behavior. However, GPT-4o, through interpretable reasoning that incorporated multiple pieces of information gathered at various stages, ultimately arrived at the correct prediction. The system's reasoning process, including an analysis of the child's interactions with toys and building blocks, was clearly documented and presented in the final explanation. In contrast, widely used scale assessment methods typically involve predefined behavioral descriptions and scoring systems, limiting their interpretability to predefined categories. These methods often require human experts to conduct subsequent quantitative analyses to derive detailed and targeted insights [48]. Conversely, the MS-RRBR dynamically generates explanations based on the specific context and nuances of the observed behaviors, which is particularly valuable in clinical settings.  
However, it is worth noting that the interpretability of MS-RRBR may be limited by the complexity of interactions between different models. Our approach involves features and outputs from multiple processing stages, and this complexity can pose challenges for comprehensively tracking and understanding the decision-making processes at each stage. Specifically, the interactions between GPT-4o and the various models encompass visual and linguistic features, as well as mathematical calculations involving spatial coordinates, making it difficult to disentangle and explain these interactions. Moreover, the fact verification step introduces an additional layer of risk to the overall framework. If Grounding Dino fails to accurately detect an object or its location, it may incorrectly negate the MLLM's answer, thus compromising overall accuracy and reliability.