用进化算法生成更新颖多样的科研点子，避免大模型思维趋同。
EvoGens: A Population-Based Heuristic Search Framework for Scientific Idea Generation
- 模仿生物进化，通过变异与交叉重组想法，持续探索新方向。
- 新颖性从0.1升至0.4，多样性从0.24提至0.55，质量保持稳定。
- 适合需要突破性创意的科研人员，尤其在自动评估下提升创意广度。
生成新颖的科研想法是科学进步的基础。尽管大语言模型（LLMs）在辅助该过程方面展现出潜力，但现有方法常出现语义收敛，导致创意多样性与新颖性不足。为此，我们提出EvoGens，一个受进化启发的框架，将科研想法生成建模为想法种群上的演化搜索。EvoGens通过基于排名的变异结合差异化检索规划引入外部知识，并采用语义感知交叉融合互补概念以实现概念重组。轻量级评估信号引导选择过程，促进持续探索并缓解过早收敛。大量实验表明，相比当前最先进基线，EvoGens显著提升了探索能力：新颖性从0.1提升至0.4，多样性从0.24提升至0.55，同时在现有自动评估协议下保持相当的想法质量。结果表明，演化机制可作为探索导向科研构思的有效框架，尤其有助于在统一自动评估设置下拓宽候选想法的新颖性与多样性。
原文摘要 · Abstract (English)
Generating novel research ideas is fundamental to scientific progress. While Large Language Models (LLMs) show promise in assisting this process, existing approaches often exhibit semantic convergence, resulting in limited diversity and novelty. To address this, we introduce EvoGens, an evolution-inspired framework that recasts scientific idea generation as an evolutionary search over a population of ideas. EvoGens iteratively applies rank-based mutation with differentiated retrieval planning to incorporate external knowledge, and semantic-aware crossover to fuse complementary concepts for conceptual reorganization. A lightweight evaluation signal guides the selection process, encouraging sustained exploration while mitigating premature convergence. Extensive experiments demonstrate that EvoGens substantially enhances exploration capabilities compared to state-of-the-art baselines. Specifically, it improves the Novelty from 0.1 to 0.4 and the Diversity from 0.24 to 0.55, while maintaining comparable idea quality under the current automatic evaluation protocol. These findings suggest that evolutionary mechanisms can serve as a useful framework for exploration-oriented research ideation, especially for broadening the novelty and diversity of candidate ideas under a shared automatic evaluation setting.
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