AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[Submitted on 1 Oct 2026] Title:Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening View a PDF of the paper titled Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening, by Ziyuan Gao View PDF HTML (experimental) Abstract:High-throughput perturbation screens increasingly use machine-learning models to predict the effects of gene combinations that were never measured. A difficult case arises when both genes in a test pair were seen individually during training, but their combination was not. In this setting, standard OOD methods often fail because neither gene appears unfamiliar on its own. We therefore compare each model prediction with a simple additive prediction obtained by summing the two observed single-gene effects. The difference between them, which we call additive disagreement (AD), measures how much extra interaction effect the model introduces for an unseen combination. Across eight prediction methods and three Perturb-seq datasets, specialised neural predictors do not consistently outperform this simple additive baseline. More importantly, predictions with larger deviations from the additive baseline tend to have larger errors. Conventional output-based and embedding-based confidence signals are inconsistent across datasets and models, whereas AD remains informative across all evaluated compositional settings. We further study how this signal can guide decisions in an agentic perturbation-screening workflow. The code is available at this https URL. Comments: Accepted at the NeurIPS 2026 Workshop ML4Molecules: Agentic Systems for Molecular Sciences Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Genomics (q-bio.GN) Cite as: arXiv:2610.18880 [cs.LG] (or arXiv:2610.18880v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.18880 arXiv-issued DOI via DataCite Submission history From: Ziyuan Gao [view email] [v1] Thu, 1 Oct 2026 02:29:02 UTC (2,117 KB) Full-text links: Access Paper: View a PDF of the paper titled Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening, by Ziyuan Gao View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG new | recent | 2026-10 Change to browse by: cs cs.AI q-bio q-bio.GN References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)