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References

  1. Brunzema, P., Tiao, L., Le, N., De Angeli, K., Xuan, Y., Gligorijevic, D. Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch. arXiv:2608.00316, 2026. No official code (this repo is an independent re-implementation). Paper

  2. Suwandi, R. C., Yin, F., Wang, J., Li, R., Chang, T.-H., Theodoridis, S. Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs. NeurIPS 2025. Paper Code

  3. Eriksson, D., Pearce, M., Gardner, J., Turner, R. D., Poloczek, M. Scalable Global Optimization via Local Bayesian Optimization. NeurIPS 2019. Paper Code

  4. Hvarfner, C., Stoll, D., Souza, A., Lindauer, M., Hutter, F., Nardi, L. πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization. ICLR 2022. Paper Code

  5. Liu, T., Astorga, N., Seedat, N., van der Schaar, M. Large Language Models to Enhance Bayesian Optimization. ICLR 2024. Paper Code

  6. Balandat, M., Karrer, B., Jiang, D. R., Daulton, S., Letham, B., Wilson, A. G., Bakshy, E. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS 2020. Paper Code

  7. Chew, R. W. T., Chen, Z., Hemachandra, A., Low, B. K. H. BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Tasks. arXiv:2605.17000, 2026. Paper Code