Solve it with EASE
It provides a transparent and extensible platform for researchers and practitioners to co-design algorithms across diverse domains, but it is incremental as it builds on existing LLM-based methods.
The paper introduces EASE, a modular framework that uses large language models to iteratively generate algorithmic solutions, integrating generation, testing, analysis, and evaluation into a reproducible feedback loop.
This paper presents EASE (Effortless Algorithmic Solution Evolution), an open-source and fully modular framework for iterative algorithmic solution generation leveraging large language models (LLMs). EASE integrates generation, testing, analysis, and evaluation into a reproducible feedback loop, giving users full control over error handling, analysis, and quality assessment. Its architecture supports the orchestration of multiple LLMs in complementary roles-such as generator, analyst, and evaluator. By abstracting the complexity of prompt design and model management, EASE provides a transparent and extensible platform for researchers and practitioners to co-design algorithms and other generative solutions across diverse domains.