Shiping Wang

Surpassing traditional Monte Carlo methods, this approach offers a stable and efficient alternative for learning neural set functions, dramatically cutting down computational costs.

Neglecting edge information in graph-aware LLMs leads to significant performance bottlenecks, but the CureLLM framework unlocks superior alignment by leveraging curvature-enhanced graph representations.