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

Sam Christian Jan Gijsen, Moritz Vinzent Seiler, Kerstin Ritter · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.20288 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

条件生成モデルは、直接のトレーニングサポートを超えて科学的予測のためにますます使用されていますか。しかし、科学的な質問は、一つの介入が他の効果を変えるかどうかを頻繁に懸念します。そのような相互作用は、全体の変異のほとんどを説明しながら、モデルが関連する科学的効果を回復することなく正確に見えるようにすることを可能にする一方で、主な関連性を持っている可能性があります。これらのモデルがどのように相互作用を学ぶか、そしてこれらのが未知の条件に成功してエクストラポラートするかは不明です。したがって、我々は、生理学的時間シリーズシミュレーター、薬物用量相互作用データ、コンビニタリアル細胞障害、および分子コンフォーマー世代を使用して、

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Hallucinating, Generalization, Artificial intelligence, Overfitting, Computer science, Machine learning

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