When a bacterial sample is exposed to several antibiotics, not every applied drug necessarily acts: if the organism is resistant to one of them, that drug leaves no morphological trace. The clinically meaningful quantity is therefore not which antibiotics were applied, but which ones were active. We show that these two are sharply decoupled in real E. coli microscopy - naively assuming the applied combination equals the active one is correct only about 37% of the time - yet existing computational tools are ill-suited to recovering the active set. Forward perturbation models such as scGen, CPA, and IMPA are designed to predict appearance from treatment, not the reverse, and inverting them degrades sharply; discriminative image classifiers tend to memorise strain- and batch-specific texture and fail to transfer across experimental replicates. We introduce AURA, which reframes the task as constrained, energy-based inverse attribution. Its central inductive bias is that the active set must be a subset of the applied set; this collapses the candidate space and lets AURA infer the active subset of applied antibiotics by decomposing residual morphology into antibiotic response atoms and selecting the subset with the lowest reconstruction energy, using no strain label at test time. AURA-E adds evidence-aware abstention, withholding a prediction when candidate explanations remain near-equally plausible. On cross-replicate transfer in an E. coli cytological profiling dataset, AURA recovers the active antibiotic combination with 95.47% exact-match accuracy.

翻译：当细菌样本暴露于多种抗生素时，并非每种施加的药物都必然发挥作用：若微生物对其中一种药物具有耐药性，则该药物不会留下任何形态学痕迹。因此，具有临床意义的量并非施加了哪些抗生素，而是哪些抗生素实际具有活性。我们证明，在真实的大肠杆菌显微镜数据中，这两者存在显著脱耦——若朴素地假设施加的药物组合即等于活性组合，其正确率仅约37%——然而现有计算工具难以恢复活性集。前向扰动模型（如scGen、CPA和IMPA）旨在根据处理预测表型变化，而非反向推断，且将其反向使用会导致性能急剧下降；判别式图像分类器往往记忆菌株特异性和批次特异性纹理，无法跨实验复制迁移。我们提出AURA，该方法将任务重构为基于能量的约束性逆向归因。其核心归纳偏置是：活性集必须是施加集的子集；这一约束压缩了候选空间，使AURA能够通过将残差形态学分解为抗生素响应原子，并选择重构能量最低的子集，从而推断施加抗生素中的活性子集——且测试时不使用任何菌株标签。AURA-E增加了证据感知的弃权机制，在候选解释保持近似同等可信度时暂不预测。在大肠杆菌细胞学分析数据集的跨复制迁移任务中，AURA以95.47%的精确匹配准确率恢复了活性抗生素组合。