CellCanon 发布：可解释、可更新的智能体细胞世界模型
CellCanon: An Agentic Cellular World Model for Prediction, Explanation, and Observation-Driven Updating
一家团队把单细胞预测、解释和证据更新做进一个智能体框架，SciPlex3 上 AUPRC 高出基线 63.3%，token 还省了 99%，做生物信息的朋友可以看看。
CellCanon 是首个智能体式细胞世界模型，统一了单细胞表达预测、证据溯源解释和观测驱动更新三项任务。在 SciPlex3 的 339 个 held-out 条件上，其差异表达 AUPRC 和 Macro-F1 分别超过各指标最强基线 63.3% 和 17.3%。相比逐基因推理，Predict 和 Explain 任务的 language-model token 消耗分别下降 99.77% 和 98.41%。在模拟实验反馈中，Update 模块每个药物仅用一条实测数据，无需改模型权重即超过最强微调基线。
CellCanon: An Agentic Cellular World Model for Prediction, Explanation, and Observation-Driven Updating
Biological discovery requires using molecular knowledge and new measurements to infer responses beyond the conditions already observed. We introduce CellCanon, the first agentic cellular world model to unify single-cell expression prediction, evidence-grounded explanation, and observation-driven updating. Its predictive core reuses condition-level biological evidence across gene-specific numerical readouts to guide single-cell expression generation. A shared world state links predictions, acquired measurements, and source-linked claims, supporting explanation and revision. On 339 held-out SciPlex3 conditions across three cell lines, CellCanon improves differential-expression AUPRC and Macro-F1 over the best baseline for each metric by 63.3% and 17.3%, respectively, and direction Macro-F1 by 7.2%. Relative to gene-wise reasoning on matched drug-cell cohorts, measured language-model token use falls by 99.77% for Predict and 98.41% for Explain. On the source-grounded benchmark, Explain correctly answers all answerable questions with supporting proofs and identifies all insufficient-evidence cases. In simulated experimental feedback, Update uses one measured source context per drug to exceed the best fine-tuned baselines in differential-expression AUPRC and Macro-F1 without changing model weights. Together, these capabilities support a cumulative account of cellular responses, with predictions and biological judgments that can be revised as evidence accumulates.