用基因组哈希记忆模块，让基因模型更高效准确地识别生物序列规律。
Beyond Conditional Computation: Retrieval-Augmented Genomic Foundation Models with Gengram
- 引入基因组专用哈希表，直接查找多碱基模式而非逐位计算
- 在多个功能基因组任务中提升性能最高达14%
- 结果可解释性强，且适配主流基因组模型
当前基因组基础模型（GFMs）依赖大量神经计算，从单碱基输入中隐式逼近保守的生物基序。我们提出Gengram，一种条件记忆模块，通过基因组特定的哈希方案，引入显式且高效的多碱基基序查找原语，建立基因组“语法”。将Gengram集成到最先进的GFMs主干网络中，可在多个功能基因组任务中实现显著提升（最高达14%）。该模块展现出强泛化能力，对Gengram潜在空间的进一步分析揭示了与基础生物学知识高度一致的有意义表示。通过将结构化的基序记忆作为建模原语，Gengram同时提升了实证性能与机制可解释性，为下一代基因组基础模型提供了一条可扩展且符合生物学逻辑的发展路径。代码开源地址：https://github.com/zhejianglab/Genos，模型检查点位于：https://huggingface.co/ZhejiangLab/Gengram。
原文摘要 · Abstract (English)
Current genomic foundation models (GFMs) rely on extensive neural computation to implicitly approximate conserved biological motifs from single-nucleotide inputs. We propose Gengram, a conditional memory module that introduces an explicit and highly efficient lookup primitive for multi-base motifs via a genomic-specific hashing scheme, establishing genomic "syntax". Integrated into the backbone of state-of-the-art GFMs, Gengram achieves substantial gains (up to 14%) across several functional genomics tasks. The module demonstrates robust architectural generalization, while further inspection of Gengram's latent space reveals the emergence of meaningful representations that align closely with fundamental biological knowledge. By establishing structured motif memory as a modeling primitive, Gengram simultaneously boosts empirical performance and mechanistic interpretability, providing a scalable and biology-aligned pathway for the next generation of GFMs. The code is available at https://github.com/zhejianglab/Genos, and the model checkpoint is available at https://huggingface.co/ZhejiangLab/Gengram.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。