Manual slide creation is labor-intensive and requires expert prior knowledge. Existing natural language-based LLM generation methods struggle to capture the visual and structural nuances of slide designs. To address this, we formalize the Reference Image to Slide Generation task and propose Slide2Code, the first benchmark with difficulty-tiered samples based on a novel Slide Complexity Metric. We introduce SlideCoder, a layout-aware, retrieval-augmented framework for generating editable slides from reference images. SlideCoder integrates a Color Gradient-based Segmentation algorithm and a Hierarchical Retrieval-Augmented Generation method to decompose complex tasks and enhance code generation. We also release SlideMaster, a 7B open-source model fine-tuned with improved reverse-engineered data. Experiments show that SlideCoder outperforms state-of-the-art baselines by up to 40.5 points, demonstrating strong performance across layout fidelity, execution accuracy, and visual consistency. Our code is available at https://github.com/vinsontang1/SlideCoder.

In this work, we tackle the challenge of multi-label emotion classification, where a sentence can simultaneously express multiple emotions. This task is particularly difficult due to the overlapping nature of emotions and the limited context available in short texts. To address these challenges, we propose an ensemble approach that integrates Pre-trained Language Models (BERT-based models) and Large Language Models, each capturing distinct emotional cues within the text. The predictions from these models are aggregated through a voting mechanism, enhancing classification accuracy. Additionally, we incorporate threshold optimization and class weighting techniques to mitigate class imbalance. Our method demonstrates substantial improvements over baseline models. Our approach ranked 4th out of 90 on the English leaderboard and exhibited strong performance in English in SemEval-2025 Task 11 Track A.