From Measured Biology to Analysis Decisions: How Far Does Post-Trained Knowledge Travel?

Closed-loop biological discovery requires models to do more than acquire domain knowledge: they must reuse prior experimental knowledge when analyzing new evidence. We study whether biological knowledge introduced through post-training can serve this role. We introduce MorphKnow, containing 15,004 experimentally measured phenotypic knowledge items derived from perturbational morphology, and MorphAnalysis, a paired suite of 967 data-analysis tasks that require combining prior biological knowledge with task-specific experimental evidence. We evaluate knowledge reuse across accessibility, inferential extension, and analytical application, and conduct controlled post-training experiments with supervised fine-tuning, distillation-based objectives, and reinforcement learning. We find a sharp separation between knowledge acquisition and downstream usability. Token-level post-training makes MorphKnow highly accessible and supports some unseen comparisons, but portability depends strongly on the operation required: it weakens for conditional lookups and held-out biological entities, and does not translate into improved scientific analysis. Supplying the correct facts does not restore analytical performance: models follow the biological knowledge but make less effective use of the current task evidence. Preserving analysis behavior during training, or explicitly prompting the model to recall the relevant fact before analysis, still does not produce reliable gains on the tasks that depend on that fact. These results suggest that parametric knowledge acquisition alone does not make biological knowledge a usable scientific prior, motivating post-training and evaluation that explicitly target knowledge-evidence integration.