ResearchEVO: An End-to-End Framework for Automated Scientific Discovery and Documentation
Zhe Zhao, Haibin Wen, Jiaming Ma, Jiachang Zhan, Tianyi Xu, Ye Wei, Qingfu Zhang

TL;DR
ResearchEVO is an end-to-end AI framework that autonomously discovers scientific algorithms and documents findings in publication-ready papers, integrating evolutionary search and literature-grounded writing.
Contribution
It is the first system to jointly perform algorithm evolution and scientific documentation in a fully automated pipeline.
Findings
Discovered novel algorithms in Quantum Error Correction and Physics-Informed Neural Networks.
Generated accurate, citation-grounded LaTeX manuscripts without hallucinations.
Abstract
An important recurring pattern in scientific breakthroughs is a two-stage process: an initial phase of undirected experimentation that yields an unexpected finding, followed by a retrospective phase that explains why the finding works and situates it within existing theory. We present ResearchEVO, an end-to-end framework that computationally instantiates this discover-then-explain paradigm. The Evolution Phase employs LLM-guided bi-dimensional co-evolution -- simultaneously optimizing both algorithmic logic and overall architecture -- to search the space of code implementations purely by fitness, without requiring any understanding of the solutions it produces. The Writing Phase then takes the best-performing algorithm and autonomously generates a complete, publication-ready research paper through sentence-level retrieval-augmented generation with explicit anti-hallucination…
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