[2603.14575] CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad

Computer Science > Machine Learning arXiv:2603.14575 (cs) [Submitted on 15 Mar 2026 (v1), last revised 29 Mar 2026 (this version, v2)] Title:CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad Authors:Yongqiang Chen, Chenxi Liu, Zhenhao Chen, Tongliang Liu, Bo Han, Kun Zhang View a PDF of the paper titled CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad, by Yongqiang Chen and 5 other authors View PDF Abstract:Evolve-based agent such as AlphaEvolve is one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific problems by iteratively improving and evolving programs, leveraging the prior knowledge and reasoning capabilities of LLMs. Despite the success, existing evolve-based agents lack targeted guidance for evolution and effective mechanisms for organizing and utilizing knowledge acquired from past evolutionary experience. Consequently, they suffer from decreasing evolution efficiency and exhibit oscillatory behavior when approaching known performance boundaries. To mitigate the gap, we develop CausalEvolve, equipped with a causal scratchpad that leverages LLMs to identify and reason about guiding factors for evolution. At the beginning, CausalEvolve first identifies outcome-level factors that offer complementary inspirations in improving the target objective. During the evolution, CausalEvolve also inspects surprise patterns during the evolution and abductive reasoning t...