## 5 Experiments
### Comparative Experiments: Proposed Method Versus Other Large-Scale Pre-Trained Models  
To systematically evaluate the structural effectiveness and performance advantages of the proposed method in the multimodal bone stick matching task, a series of comparative experiments were carefully designed. To enable a consistent and controlled comparison under unified experimental settings, we concurrently incorporated two representative state  
Figure 10: Cumulative Match Characteristic (CMC) curves of different matching methods: (**a**) CMC curves on Test Set 1, illustrating performance under ideal matching conditions. (**b**) CMC curves on Test Set 2, highlighting robustness under data heterogeneity and distributional shifts.  
of-the-art multimodal pretrained model architectures--ViLT-Base and VLMo-Base--as benchmarking baselines. Both models were trained on the same dataset used for the large-scale pretrained models in this study, thereby ensuring a consistent training context for evaluation. Given that these models adopt a fusion input architecture--where image and text tokens are concatenated prior to encoding, with the requirement that visual and textual inputs must be correspondingly paired and fed simultaneously--this study constructs a comparison framework from a dual-encoder perspective to maintain alignment in model structure and ensure the validity of experimental conclusions. The experimental design is as follows: In Experiments (1) to (3), the matching task is conducted based on image features of bone stick fracture regions and textual features from bone stick transcriptions. Specifically, Experiment (3) adopts a direct feature concatenation strategy, where the fracture image features extracted by Vision-RUKV-L model are concatenated with the semantic features of the transcriptions extracted by the RWKV-6 model. Experiments (4) to (6) build upon the previous configuration by incorporating archeological metadata of bone sticks, extracted using BERT-base-cased, into the feature representation, thereby forming an enhanced multimodal matching representation. Experiments (7) to (9) further modify the previous setting by replacing the conventional feature concatenation module with a dynamic cross-modal fusion module, aimed at investigating the potential improvements in matching accuracy introduced by more advanced interaction mechanisms. The detailed results of these experiments are reported in Table 4.  
As shown in the table, although ViLT-Large and VLMo-Base possess substantially larger parameter scales compared to the baseline model proposed in this study, their matching accuracy on both test sets is consistently inferior, and they incur significantly longer inference time. This highlights the substantial impact of model architecture on inference efficiency. In the second group of experiments, the inclusion of archeological metadata resulted in a 2-3% increase in average matching accuracy across all models, validating the importance of semantic prior knowledge in the bone stick matching task. However, for Transformer-based architectures, this enhancement introduced additional computational overhead, further prolonging inference time and diminishing their real-time adaptability. Results from the third group of experiments indicate that incorporating the Dynamic Hybrid Cross-Modal Fusion Module into the matching process yields an additional 3-6% increase in overall matching accuracy. This underscores the effectiveness of the module in enhancing multimodal information interaction, particularly in optimizing feature fusion for large-scale models applied to domain-specific tasks. Moreover, on the more challenging Test Set 2, Transformer-based models exhibit a marked decline in matching accuracy, further exposing their limitations in robustness. Notably, their inference time is nearly twice that of the proposed method, reinforcing the practical advantages of our approach under complex and noise-prone conditions.