LLM-Based Program Synthesis

This field studies how language models can turn descriptions, examples, or formal specifications into programs, algorithms, and executable workflows. Common approaches combine prompting or specialized intermediate languages with retrieval, multi-agent decomposition, evolutionary search, and feedback from execution, solvers, compilers, or formal verifiers. These methods are used for tasks ranging from benchmark coding and Dafny or Verilog generation to optimization, GPU kernels, robotics, and CAD; evaluation often tests execution and correctness on suites such as LiveCodeBench, KernelBench, and VerilogEval. Systems can produce useful domain-specific code, but passing tests or compiling does not ensure semantic correctness, safety, performance, or generalization to unseen cases.