AI 中文总结
研究旨在解决自动化科学发现中语言模型与生物物理的认知差距。通过多尺度自主发现引擎Octopus，结合大语言模型群体和算法物理引擎，针对结直肠癌转录组进行无监督扫描，发现IGF2是5-氟尿嘧啶耐药脆弱性，建立了可验证的生物医学发现范式。
AI 中文摘要
自动化科学发现的加速受到大语言模型语义推理与哺乳动物生物学确定性物理之间认知差距的根本制约。近期多智能体框架虽能自主生成假设和进行体外实验分析，但缺乏多尺度临床转化所需的数学基础因果约束。算法临床数字双胞胎虽能预测生物状态，但依赖黑箱潜在空间。本文引入多尺度自主发现引擎（Octopus），将零泄漏局部大语言模型群体与严格算法物理引擎结合。该系统针对体外CRISPR依赖性数据自主生成治疗假设，利用机制可解释性追踪动态因果级联，并在计算机模拟中正交转化新出现的脆弱性以预测体内哺乳动物肿瘤轨迹和人类总生存期。在对结直肠癌转录组的完全无监督扫描中，该流程自主识别出胰岛素样生长因子2（IGF2）是5-氟尿嘧啶耐药的严格受限脆弱性。经严格错误发现率校正后该发现仍具显著性，并成功预测独立小鼠队列中体内肿瘤体积显著缩小。此框架弥合了多智能体推理与数学约束临床生存之间的差距，为自动化端到端生物医学发现建立了可验证的零泄漏范式。
英文摘要
The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology. While recent multi-agent frameworks have achieved autonomous hypothesis generation and in vitro experimental analysis, they lack the mathematically grounded, causal constraints required for multi-scale clinical translation. Furthermore, while algorithmic clinical digital twins successfully forecast biological states, they rely on black-box latent spaces, sacrificing mechanistic interpretability for predictive accuracy. Here, we introduce the Multi-Scale Autonomous Discovery Engine (Octopus), a neuro-symbolic architecture that unites zero-leakage, local LLM swarms with strict algorithmic physics engines. Rather than stopping at isolated cellular assays, the system autonomously generated therapeutic hypotheses against in vitro CRISPR dependency data (CCLE), traced dynamic causal cascades using mechanistic interpretability (XGBoost SHAP vectors), and orthogonally translated the emergent vulnerabilities in silico to predict in vivo mammalian tumor trajectory (PDX) and human overall survival (Marisa). In a fully unsupervised sweep of colorectal cancer transcriptomes, the pipeline autonomously identified Insulin-like Growth Factor 2 (IGF2) as a strictly bounded vulnerability to 5-Fluorouracil resistance. The discovery maintained significance after rigorous Benjamini-Hochberg false discovery rate correction (q=0.0292, Log-Rank p=0.0007 ) and successfully predicted significant in vivo tumor volume shrinkage in an independent mouse cohort (Mann-Whitney p=0.0373). By bridging the chasm between multi-agent reasoning and mathematically bounded clinical survival, this framework establishes a verifiable, zero-leakage paradigm for automated, end-to-end biomedical discovery.
