4 accepted papers
Despite rapid advances in code generation, current Large Language Models (LLMs) still lack an essential capability for reliable and verifiable code generation: compositional reasoning across multi-function programs. To explore this potential and important gap, we introduce DafnyCOMP, a benchmark des…
Formal verification is the next frontier for ensuring the correctness of code generated by Large Language Models (LLMs). While methods that co-generate code and formal specifications in formal languages, like Dafny, can, in principle, prove alignment with user intent, progress is bottlenecked by sp…
In recent years, Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence. However, training these models from scratch requires substantial computational resources and vast amounts of text data. In this paper, we explores an alternative approach to construct…
The absolute positioning accuracy of robots is a primary factor limiting their applications. In order to enhance the efficiency and precision of robot calibration, this study introduces a method that utilizes the Levy flight and Sparrow Search Algorithm (LSSA) to optimize the measurement pose of the