Solve it with EASE
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914051293970432 |
|---|---|
| author | Viktorin, Adam Kadavy, Tomas Kovac, Jozef Pluhacek, Michal Senkerik, Roman |
| author_facet | Viktorin, Adam Kadavy, Tomas Kovac, Jozef Pluhacek, Michal Senkerik, Roman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18108 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solve it with EASE Viktorin, Adam Kadavy, Tomas Kovac, Jozef Pluhacek, Michal Senkerik, Roman Machine Learning Artificial Intelligence I.2.2; I.2.7 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. |
| title | Solve it with EASE |
| topic | Machine Learning Artificial Intelligence I.2.2; I.2.7 |
| url | https://arxiv.org/abs/2509.18108 |