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In this white paper, we present AlphaEvolve, an evolutionary coding agent that substantially enhances capabilities of state-of-the-art LLMs on highly challenging tasks such as tackling open scientific problems or optimizing critical pieces of computational infrastructure.
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The bilinear complexity and practical algorithms for matrix multiplication
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Neural machine translation by jointly learning to align and translate
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Illuminating search spaces by mapping elites
J.-B. Mouret and J. Clune · 2015
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Large-scale cluster management at Google with Borg
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The minimum overlap problem revisited
J. K. Haugland · 2016
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On suprema of autoconvolutions with an application to Sidon sets
A. Cloninger and S. Steinerberger · 2017
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Hermite polynomials, linear flows on the torus, and an uncertainty principle for roots
F. Gonçalves, D. O. e Silva, and S. Steinerberger · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang · 2018
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An optimal uncertainty principle in twelve dimensions via modular forms
H. Cohn and F. Gonçalves · 2019
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Advancing mathematics by guiding human intuition with AI
A. Davies, P. Veličković, L. Buesing, S. Blackwell, D. Zheng, N. Tomašev, R. Tanburn, P. Battaglia, C. Blundell, A. Juhász, M. Lackenby, G. Williamson, D. Hassabis, and P. Kohli · 2021
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Highly accurate protein structure prediction with AlphaFold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma, and R. Fergus · 2021
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Several bilinear algorithms for matrix multiplication problems ⟨ 3 , P , Q ⟩ \langle 3,{P},{Q}\rangle
A. Smirnov · 2021
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Genetic Programming for Production Scheduling
F. Zhang, S. Nguyen, Y. Mei, and M. Zhang · 2021
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Flashattention: Fast and memory-efficient exact attention with io-awareness
T. Dao, D. Fu, S. Ermon, A. Rudra, and C. Ré · 2022
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Discovering faster matrix multiplication algorithms with reinforcement learning
A. Fawzi, M. Balog, A. Huang, T. Hubert, B. Romera-Paredes, M. Barekatain, A. Novikov, F. J. R. Ruiz, J. Schrittwieser, G. Swirszcz, D. Silver, D. Hassabis, and P. Kohli · 2022
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Controllable protein design with language models
N. Ferruz and B. Höcker · 2022
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M. Ganzhinov · 2022
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Competition-level code generation with AlphaCode
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. D. Lago, T. Hubert, P. Choy, C. de Masson d’Autume, I. Babuschkin, X. Chen, P.-S. Huang, J. Welbl, S. Gowal, A. Cherepanov, J. Molloy, D. J. Mankowitz, E. S. Robson, P. Kohli, N. de Freitas, K. Kavukcuoglu, and O. Vinyals · 2022
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Evolving symbolic density functionals
H. Ma, A. Narayanaswamy, P. Riley, and L. Li · 2022
Cited alongside, same era.
Bilinear algorithm for matrix multiplication ⟨ 4 × 4 × 9 ; 104 ⟩ \langle 4\times 4\times 9;104\rangle . an irreducibly irrational solution of the brent system?
A. Smirnov · 2022
Cited alongside, same era.
Autonomous chemical research with large language models
D. A. Boiko, R. MacKnight, B. Kline, and G. Gomes · 2023
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Interpretable machine learning for science with pysr and symbolicregression. jl
M. Cranmer · 2023
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Promptbreeder: Self-referential self-improvement via prompt evolution
C. Fernando, D. Banarse, H. Michalewski, S. Osindero, and T. Rocktäschel · 2023
Cited alongside, same era.
Amplifying human performance in combinatorial competitive programming
P. Veličković, A. Vitvitskyi, L. Markeeva, B. Ibarz, L. Buesing, M. Balog, and A. Novikov · 2024
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SciMON: Scientific inspiration machines optimized for novelty
Q. Wang, D. Downey, H. Ji, and T. Hope · 2024
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Large language models for automated open-domain scientific hypotheses discovery
Z. Yang, X. Du, J. Li, J. Zheng, S. Poria, and E. Cambria · 2024
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ReEvo: Large language models as hyper-heuristics with reflective evolution
H. Ye, J. Wang, Z. Cao, F. Berto, C. Hua, H. Kim, J. Park, and G. Song · 2024
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HoneyComb: A flexible LLM-based agent system for materials science
H. Zhang, Y. Song, Z. Hou, S. Miret, and B. Liu · 2024
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Hypothesis generation with large language models
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Flip graphs for matrix multiplication
M. Kauers and J. Moosbauer · 2023
Cited alongside, same era.
Learning skillful medium-range global weather forecasting
R. Lam, A. Sanchez-Gonzalez, M. Willson, P. Wirnsberger, M. Fortunato, F. Alet, S. Ravuri, T. Ewalds, Z. Eaton-Rosen, W. Hu, A. Merose, S. Hoyer, G. Holland, O. Vinyals, J. Stott, A. Pritzel, S. Mohamed, and P. Battaglia · 2023
Cited alongside, same era.
Evolution through large models
J. Lehman, J. Gordon, S. Jain, K. Ndousse, C. Yeh, and K. O. Stanley · 2023
Cited alongside, same era.
Evolutionary-scale prediction of atomic-level protein structure with a language model
Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, N. Smetanin, R. Verkuil, O. Kabeli, Y. Shmueli, A. dos Santos Costa, M. Fazel-Zarandi, T. Sercu, S. Candido, and A. Rives · 2023
Cited alongside, same era.
Large language models generate functional protein sequences across diverse families
A. Madani, B. Krause, E. R. Greene, S. Subramanian, B. P. Mohr, J. M. Holton, J. L. Olmos, C. Xiong, Z. Z. Sun, R. Socher, J. S. Fraser, and N. Naik · 2023
Cited alongside, same era.
Faster sorting algorithms discovered using deep reinforcement learning
D. J. Mankowitz, A. Michi, A. Zhernov, M. Gelmi, M. Selvi, C. Paduraru, E. Leurent, S. Iqbal, J.-B. Lespiau, A. Ahern, T. Köppe, K. Millikin, S. Gaffney, S. Elster, J. Broshear, C. Gamble, K. Milan, R. Tung, M. Hwang, T. Cemgil, M. Barekatain, Y. Li, A. Mandhane, T. Hubert, J. Schrittwieser, D. Hassabis, P. Kohli, M. Riedmiller, O. Vinyals, and D. Silver · 2023
Cited alongside, same era.
Mathematical discoveries from program search with large language models
B. Romera-Paredes, M. Barekatain, A. Novikov, M. Balog, M. P. Kumar, E. Dupont, F. J. R. Ruiz, J. Ellenberg, P. Wang, O. Fawzi, P. Kohli, and A. Fawzi · 2023
Cited alongside, same era.
Y. Zhou, H. Liu, T. Srivastava, H. Mei, and C. Tan · 2024
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FunBO: Discovering acquisition functions for Bayesian optimization with FunSearch
V. Aglietti, I. Ktena, J. Schrouff, E. Sgouritsa, F. J. R. Ruiz, A. Malek, A. Bellot, and S. Chiappa · 2025
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Chemical reasoning in LLMs unlocks steerable synthesis planning and reaction mechanism elucidation
A. M. Bran, T. A. Neukomm, D. P. Armstrong, Z. Jončev, and P. Schwaller · 2025
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A review of large language models and autonomous agents in chemistry
M. Caldas Ramos, C. J. Collison, and A. D. White · 2025
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Discovering symbolic cognitive models from human and animal behavior
P. S. Castro, N. Tomasev, A. Anand, N. Sharma, R. Mohanta, A. Dev, K. Perlin, S. Jain, K. Levin, N. Éltető, W. Dabney, A. Novikov, G. C. Turner, M. K. Eckstein, N. D. Daw, K. J. Miller, and K. L. Stachenfeld · 2025
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Automating GPU kernel generation with DeepSeek-R1 and inference time scaling, 2025
T. Chen, B. Xu, and K. Devleker · 2025
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Generative modelling for mathematical discovery
J. S. Ellenberg, C. S. Fraser-Taliente, T. R. Harvey, K. Srivastava, and A. V. Sutherland · 2025
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Erich’s Packing Center
E. Friedman · 2025
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Discovering emergent connections in quantum physics research via dynamic word embeddings
F. Frohnert, X. Gu, M. Krenn, and E. van Nieuwenburg · 2025
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Gemini 2.5: Our most intelligent AI model, 2025
Gemini team · 2025
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J. Gottweis, W.-H. Weng, A. Daryin, T. Tu, A. Palepu, P. Sirkovic, A. Myaskovsky, F. Weissenberger, K. Rong, R. Tanno, K. Saab, D. Popovici, J. Blum, F. Zhang, K. Chou, A. Hassidim, B. Gokturk, A. Vahdat, P. Kohli, Y. Matias, A. Carroll, K. Kulkarni, N. Tomasev, Y. Guan, V. Dhillon, E. D. Vaishnav, B. Lee, T. R. D. Costa, J. R. Penadés, G. Peltz, Y. Xu, A. Pawlosky, A. Karthikesalingam, and V. Natarajan · 2025
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Agentic AI for scientific discovery: A survey of progress, challenges, and future directions
M. Gridach, J. Nanavati, C. Mack, K. Z. E. Abidine, and L. Mendes · 2025
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A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wang, Q. Chen, W. Peng, X. Feng, B. Qin, et al · 2025
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Consequences of the Moosbauer-Poole algorithms
M. Kauers and I. Wood · 2025
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The AI CUDA engineer: Agentic CUDA kernel discovery, optimization and composition
R. T. Lange, A. Prasad, Q. Sun, M. Faldor, Y. Tang, and D. Ha · 2025
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A large language model framework for literature-based disease–gene association prediction
P.-H. Li, Y.-Y. Sun, H.-F. Juan, C.-Y. Chen, H.-K. Tsai, and J.-H. Huang · 2025
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ECO: An LLM-driven efficient code optimizer for warehouse scale computers
H. Lin, M. Maas, M. Roquemore, A. Hasanzadeh, F. Lewis, Y. Simonson, T.-W. Yang, A. Yazdanbakhsh, D. Altinbüken, F. Papa, et al · 2025
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Flip graphs with symmetry and new matrix multiplication schemes
J. Moosbauer and M. Poole · 2025
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DORA AI scientist: Multi-agent virtual research team for scientific exploration discovery and automated report generation
V. Naumov, D. Zagirova, S. Lin, Y. Xie, W. Gou, A. Urban, N. Tikhonova, K. Alawi, M. Durymanov, F. Galkin, S. Chen, D. Sidorenko, M. Korzinkin, M. Scheibye-Knudsen, A. Aspuru-Guzik, E. Izumchenko, D. Gennert, F. W. Pun, M. Zhang, P. Kamya, A. Aliper, F. Ren, and A. Zhavoronkov · 2025
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Introducing OpenAI o3 and o4-mini, 2025
OpenAI · 2025
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Quantum many-body physics calculations with large language models
H. Pan, N. Mudur, W. Taranto, M. Tikhanovskaya, S. Venugopalan, Y. Bahri, M. P. Brenner, and E.-A. Kim · 2025
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Accelerating Earth science discovery via multi-agent LLM systems
D. Pantiukhin, B. Shapkin, I. Kuznetsov, A. A. Jost, and N. Koldunov · 2025
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Towards scientific intelligence: A survey of LLM-based scientific agents
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Quantum circuit optimization with AlphaTensor
F. J. R. Ruiz, T. Laakkonen, J. Bausch, M. Balog, M. Barekatain, F. J. H. Heras, A. Novikov, N. Fitzpatrick, B. Romera-Paredes, J. van de Wetering, A. Fawzi, K. Meichanetzidis, and P. Kohli · 2025
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LLM-SR: Scientific equation discovery via programming with large language models
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Can LLMs generate novel research ideas? a large-scale human study with 100+ NLP researchers
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LLM-Feynman: Leveraging large language models for universal scientific formula and theory discovery
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Algorithm discovery with LLMs: Evolutionary search meets reinforcement learning
A. Surina, A. Mansouri, L. Quaedvlieg, A. Seddas, M. Viazovska, E. Abbe, and C. Gulcehre · 2025
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GRAIL: Graph edit distance and node alignment using llm-generated code
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Efficient evolutionary search over chemical space with large language models
H. Wang, M. Skreta, C. T. Ser, W. Gao, L. Kong, F. Strieth-Kalthoff, C. Duan, Y. Zhuang, Y. Yu, Y. Zhu, Y. Du, A. Aspuru-Guzik, K. Neklyudov, and C. Zhang · 2025
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Nature language model: Deciphering the language of nature for scientific discovery
Y. Xia, P. Jin, S. Xie, L. He, C. Cao, R. Luo, G. Liu, Y. Wang, Z. Liu, Y.-J. Chen, Z. Guo, Y. Bai, P. Deng, Y. Min, Z. Lu, H. Hao, H. Yang, J. Li, C. Liu, J. Zhang, J. Zhu, R. Bi, K. Wu, W. Zhang, K. Gao, Q. Pei, Q. Wang, X. Liu, Y. Li, H. Zhu, Y. Lu, M. Ma, Z. Wang, T. Xie, K. Maziarz, M. Segler, Z. Yang, Z. Chen, Y. Shi, S. Zheng, L. Wu, C. Hu, P. Dai, T.-Y. Liu, H. Liu, and T. Qin · 2025
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