Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects

Abstract:Conditional generative models are increasingly used for scientific prediction beyond the direct training support. Yet scientific questions often concern whether one intervention changes the effect of another. Such interactions can be of primary interest while explaining little of the total variation, allowing models to appear accurate overall without recovering the relevant scientific effect. It remains unclear how these models learn interactions and when these successfully extrapolate to unseen conditions. We therefore study interaction recovery in flow-matching models using a physiological time-series simulator, drug dose-response data, combinatorial cell perturbations, and molecular conformer generations, where we separate recovery from hallucinated interaction structure. In the controlled setting, individual effects are learned much earlier than interactions and models hallucinate interactions where none exist. Across settings, we find qualitatively different generalization problems, with successful prediction depending on whether condition structure continues from observed to held-out targets. When it does, interactions are recoverable. If not, models can generate plausible responses while missing the interaction. We furthermore find that hallucinations increase as targets become less identifiable, and that successful interaction recovery in output space can fail under nonlinear readouts. Finally, we show that aggregate fidelity metrics can conceal whether models learn and extrapolate interactions.

Accepted as an oral at NeurIPS 2026 Workshop: AI Data Readiness for Scientific Discovery (AIDaR). Code: this https URL

Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)

arXiv:2610.20288 [cs.LG]

(or arXiv:2610.20288v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.20288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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