Xinyi Li

Parallel multi-anchor retrieval can dramatically improve the accuracy of legal liability determinations in traffic law by overcoming the limitations of traditional single-axis approaches.

Achieving 97% global executability in robotic assembly, this framework seamlessly integrates cognitive decision-making with physical execution, overcoming traditional limitations.

In high-stakes health contexts, stakeholders demand that trustworthiness in AI systems be inspectable, not just asserted, reshaping how we design health information tools.

HeterSEED achieves state-of-the-art performance on heterophilic heterogeneous graphs by decoupling semantic and structural information, offering a more robust approach than relying on feature similarity alone.

A single system can now generate detection rules across diverse contexts and languages, outperforming pure LLM generation by leveraging dual semantic projection spaces.