Siheng Chen

Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (MCP) ecosystem remains limited. Existing MCP research covers few servers, depends on costly manual curation, and lack...

The scalability of high-quality online education is hindered by the high costs and slow cycles of manual content creation.Despite advancements in video generation, current approaches often fail to ensure pedagogical structure and precise control due to their pixel-level, black-box nature.In this...

Large language model agents increasingly rely on memory to support long-horizon interaction, yet existing frameworks expose only a small set of low-level primitives and lack a formal, executable specification for memory control. As a result, higher-order operations such as promotion, consolidatio...

Training GUI agents with traditional centralized methods faces significant cost and scalability challenges. Federated learning (FL) offers a promising solution, yet its potential is hindered by the lack of benchmarks that capture real-world, cross-platform heterogeneity. To bridge this gap, we in...

Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. While learning from few-shot demonstrations is an emerging solution, its progress is hindered by two critical gaps: the lack of a comprehensive benchmark for systematic evalua...

Aligning Vision-Language Models (VLMs) with safety standards is essential to mitigate risks arising from their multimodal complexity, where integrating vision and language unveils subtle threats beyond the reach of conventional safeguards. Inspired by the insight that reasoning across modalities...