Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep
  Research
Large language models hold promise as scientific assistants, yet existing
agents either rely solely on algorithm evolution or on deep research in
isolation, both of which face critical limitations. Pure algorithm evolution,
as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly
plateaus in complex domains, while pure deep research proposes ideas without
validation, resulting in unrealistic or unimplementable solutions. We present
DeepEvolve, an agent that integrates deep research with algorithm evolution,
uniting external knowledge retrieval, cross-file code editing, and systematic
debugging under a feedback-driven iterative loop. Each iteration not only
proposes new hypotheses but also refines, implements, and tests them, avoiding
both shallow improvements and unproductive over-refinements. Across nine
benchmarks in chemistry, mathematics, biology, materials, and patents,
DeepEvolve consistently improves the initial algorithm, producing executable
new algorithms with sustained gains. By bridging the gap between unguided
evolution and research without grounding, DeepEvolve provides a reliable
framework for advancing scientific algorithm discovery. Our code is available
at https://github.com/liugangcode/deepevolve.
68e675e6975ac4c405ef2311
Gang Liu
68e675e6975ac4c405ef2312
Yihan Zhu
68e675e6975ac4c405ef2313
Jie Chen
68e675e6975ac4c405ef2314
Meng Jiang
Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep
  Research
Large language models hold promise as scientific assistants, yet existing
agents either rely solely on algorithm evolution or on deep research in
isolation, both of which face critical limitations. Pure algorithm evolution,
as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly
plateaus in complex domains, while pure deep research proposes ideas without
validation, resulting in unrealistic or unimplementable solutions. We present
DeepEvolve, an agent that integrates deep research with algorithm evolution,
uniting external knowledge retrieval, cross-file code editing, and systematic
debugging under a feedback-driven iterative loop. Each iteration not only
proposes new hypotheses but also refines, implements, and tests them, avoiding
both shallow improvements and unproductive over-refinements. Across nine
benchmarks in chemistry, mathematics, biology, materials, and patents,
DeepEvolve consistently improves the initial algorithm, producing executable
new algorithms with sustained gains. By bridging the gap between unguided
evolution and research without grounding, DeepEvolve provides a reliable
framework for advancing scientific algorithm discovery. Our code is available
at https://github.com/liugangcode/deepevolve.
DeepEvolve integrates deep research with algorithm evolution to propose, refine, implement, and test new hypotheses, improving initial algorithms across various scientific domains.
large language models algorithm evolution deep research external knowledge retrieval cross-file code editing systematic debugging feedback-driven iterative loop executable new algorithms scientific algorithm discovery