Distribution-Aware Programming: Learning Specialized Solvers from Experience
The research introduces distribution-aware programming to create specialized solvers by learning structural shortcuts from sample data. By using an LLM agent, the system infers reusable patterns ('solver hints') from input instances and compiles them into highly efficient code. This allows general solvers to become tailored, significantly improving runtime and quality on future problems drawn from the same distribution.