## 5 Experiments
### Ablation Study (Effect of Model Variants)  
To comprehensively evaluate the effectiveness of the three categories of features and the fusion module in the bone stick matching task, as well as to explore the impact of model parameter configurations on matching performance, a series of comparative experiments were meticulously designed and conducted. For each experimental configuration, the model's parameter size (Params/M) and its inference time on a standardized testing platform (TD/S) were recorded, enabling a holistic assessment of the trade-off between accuracy and computational efficiency across different model configurations. The experimental setup is structured as follows: Experiments (1) to (3) are based on unimodal image input, where fracture features of bone stick images are extracted using three different scales of the Vision-RWKV model--Vision-RWKV-S, Vision-RWKV-B, and Vision-RWKV-L (used in this study)--for direct matching tasks. Experiments (4) to (6) extend the setup by incorporating textual features extracted by the RWKV language models--RWKV-4, RWKV-5, and the RWKV-6--used in this work. These textual features are simply and directly concatenated with the visual features extracted by Vision-RWKV-L, forming a basic bimodal fusion architecture for matching. Experiments (7) and (8) further introduce archeological metadata features encoded using the BERT family of models--BERT-medium and the BERT-base-cased model employed in this study. These semantic features are simply and directly concatenated with the image and inscription features extracted by Vision-RWKV-L and RWKV-6, respectively, to construct a tri-modal matching framework. Experiment (9) corresponds to the full method proposed in this paper. The detailed results of all experiments are presented in Table 3, where the impacts of different feature combinations and model architectures on matching accuracy, inference time, and parameter size are systematically compared. As observed from the experimental results presented in Table 3, in the configuration where bone stick matching relies solely on image features extracted from fracture surfaces (Experiments (1) to (3)), the model's feature extraction capability improves progressively with the increasing parameter size, leading to a corresponding upward trend in matching accuracy. However, due to the unimodal nature of this approach, the overall matching performance remains constrained, and the overall accuracy level is relatively limited. Further improvements are observed when inscription features are introduced and fused with visual features (Experiments (4) to (6)), resulting in a notable average increase in approximately 5% to 8% in matching accuracy. This clearly highlights the critical role of textual information in the bone stick matching task. Moreover, this multimodal fusion strategy maintains stable inference efficiency, with only a slight increase in inference time on the test set and minimal additional demands on computational resources. Building upon this, when the archeological metadata of bone sticks is further incorporated (Experiments (7) and (8)), and all three types of features--visual, textual, and archeological--are concatenated in the feature space,  
Figure 9: Results of contrastive learning: (**a**) Training and validation loss curves. (**b**) Matching accuracy from Rank-1 to Rank-15 on Test Set 1.  
the model achieves an additional 2% to 4% improvement in matching accuracy over the previous stage. To further clarify the specific contributions of each module in the bone stick matching task, a series of comparative experiments were designed to analyze the effects of different feature fusion strategies. Experiments (9) and (10) involve the fusion of transcriptional semantic features extracted by the RWKV-6 model with archeological metadata derived using the BERT-base-cased model. Experiment (9) applies a direct concatenation of feature vectors for matching, while Experiment (10) performs matching after applying static averaging.