Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research

• URL: http://arxiv.org/abs/2510.06056v1
• Date: Tue, 07 Oct 2025 15:49:51 GMT
• Title: Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research
• Authors: Gang Liu, Yihan Zhu, Jie Chen, Meng Jiang,
• Abstract summary: We present DeepEvolve, an agent that integrates deep research with algorithm evolution.<n>Each not only proposes new hypotheses but also refines, implements, and tests them, avoiding both shallow improvements and unproductive over-refinements.<n>Across nine benchmarks in chemistry, mathematics, biology, materials, and patents, DeepEvolve consistently improves the initial algorithm.
• Score: 23.532524147608253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• EvoX: Meta-Evolution for Automated Discovery [115.89434419482797]
  EvoX is an adaptive evolution method that optimize its own evolution process.<n>It continuously updates how prior solutions are selected and varied based on progress.<n>It outperforms existing AI-driven evolutionary methods including AlphaEvolve, OpenEvolve, GEPA, and ShinkaEvolve on the majority of tasks.
  arXiv  Detail & Related papers  (2026-02-26T18:54:41Z)
• ThetaEvolve: Test-time Learning on Open Problems [110.5756538358217]
  We introduce ThetaEvolve, an open-source framework that simplifies and extends AlphaEvolve to efficiently scale both in-context learning and Reinforcement Learning (RL) at test time.<n>We find that ThetaEvolve with RL at test-time consistently outperforms inference-only baselines.
  arXiv  Detail & Related papers  (2025-11-28T18:58:14Z)
• GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms [7.228213026504935]
  GigaEvo is an open-source framework that enables researchers to study and experiment with hybrid LLM-evolution approaches.<n>We provide detailed descriptions of system architecture, implementation decisions, and experimental methodology to support further research.
  arXiv  Detail & Related papers  (2025-11-17T14:44:47Z)
• AlphaResearch: Accelerating New Algorithm Discovery with Language Models [60.502137348923156]
  Large language models have made significant progress in complex but easy-to-verify problems, yet they still struggle with discovering the unknown.<n>We present textbfAlphaResearch, an autonomous research agent designed to discover new algorithms on open-ended problems.
  arXiv  Detail & Related papers  (2025-11-11T18:03:22Z)
• CodeEvolve: An open source evolutionary coding agent for algorithm discovery and optimization [0.6198237241838559]
  We introduce CodeEvolve, an open-source evolutionary coding agent that unites Large Language Models with genetic algorithms to solve complex computational problems.<n>Our framework adapts powerful evolutionary concepts to the Large Language Models domain, building upon recent methods for generalized scientific discovery.<n>We conduct a rigorous evaluation of CodeEvolve on a subset of the mathematical benchmarks used to evaluate Google DeepMind's closed-source AlphaEvolve.
  arXiv  Detail & Related papers  (2025-10-15T22:58:06Z)
• GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models [49.257706111340134]
  We introduce GeoEvolve, a multi-agent LLM framework that couples evolutionary search with geospatial domain knowledge.<n>We evaluate it on two fundamental and classical tasks: spatial (kriging) and spatial uncertainty.<n>It reduces spatial error (RMSE) by 13-21% and enhances uncertainty estimation performance by 17%.
  arXiv  Detail & Related papers  (2025-09-25T21:03:57Z)
• AlphaEvolve: A coding agent for scientific and algorithmic discovery [63.13852052551106]
  We present AlphaEvolve, an evolutionary coding agent that substantially enhances capabilities of state-of-the-art LLMs.<n>AlphaEvolve orchestrates an autonomous pipeline of LLMs, whose task is to improve an algorithm by making direct changes to the code.<n>We demonstrate the broad applicability of this approach by applying it to a number of important computational problems.
  arXiv  Detail & Related papers  (2025-06-16T06:37:18Z)
• Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning [12.037588566211348]
  We propose to augment evolutionary search by continuously refining the search operator through reinforcement learning (RL) fine-tuning.<n>Our experiments demonstrate that integrating RL with evolutionary search accelerates the discovery of superior algorithms.
  arXiv  Detail & Related papers  (2025-04-07T14:14:15Z)
• Towards Understanding How Knowledge Evolves in Large Vision-Language Models [55.82918299608732]
  We investigate how multimodal knowledge evolves and eventually induces natural languages in Large Vision-Language Models (LVLMs)<n>We identify two key nodes in knowledge evolution: the critical layers and the mutation layers, dividing the evolution process into three stages: rapid evolution, stabilization, and mutation.<n>Our research is the first to reveal the trajectory of knowledge evolution in LVLMs, providing a fresh perspective for understanding their underlying mechanisms.
  arXiv  Detail & Related papers  (2025-03-31T17:35:37Z)
• Improving RNA Secondary Structure Design using Deep Reinforcement Learning [69.63971634605797]
  We propose a new benchmark of applying reinforcement learning to RNA sequence design, in which the objective function is defined to be the free energy in the sequence's secondary structure. We show results of the ablation analysis that we do for these algorithms, as well as graphs indicating the algorithm's performance across batches.
  arXiv  Detail & Related papers  (2021-11-05T02:54:06Z)
• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch [76.83052807776276]
  We show that it is possible to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction in the field.
  arXiv  Detail & Related papers  (2020-03-06T19:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.