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[Submitted on 29 Sep 2026] Title:Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs View a PDF of the paper titled Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs, by Hung-Jen Chen and 6 other authors View PDF HTML (experimental) Abstract:Generative vision-language models (VLMs) such as Qwen-VL and LLaVA achieve strong zero-shot performance on tasks overlapping with their pretraining distribution, yet fail on specialized domains where the required discriminative features were never learned, a regime we term distant out-of-distribution (OOD). Standard adaptation methods cannot overcome this representational absence because they operate within the encoder's existing feature space. However, VLMs retain a robust descriptive capacity even when discrimination collapses: a model that cannot classify a medical scan can still articulate its visual patterns. Exploiting this asymmetry, we introduce Inductive Visual Logic (IVL), a training-free framework that constructs classification knowledge from the model's surviving descriptive ability. IVL extracts visual traits from few-shot support images through dual-mode prompting, combining semantic descriptions with primitive visual observations, and organizes them into per-class trait dictionaries. At inference, hierarchical filtering identifies spatially grounded trait evidence for classification. Across multiple distant-OOD benchmarks, IVL achieves the highest aggregate accuracy under two VLM backbones while producing interpretable, trait-traceable predictions. Comments: Accepted by ECCV 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2609.38362 [cs.CV] (or arXiv:2609.38362v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.38362 arXiv-issued DOI via DataCite Journal reference: Computer Vision - ECCV 2026, Lecture Notes in Computer Science, vol. 17077, pp. 429-447, Springer, 2026 Related DOI: https://doi.org/10.1007/978-3-032-37041-9_23 DOI(s) linking to related resources Submission history From: Hung-Jen Chen [view email] [v1] Tue, 29 Sep 2026 18:25:04 UTC (19,947 KB) Full-text links: Access Paper: View a PDF of the paper titled Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs, by Hung-Jen Chen and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-09 Change to browse by: cs 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?) 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?)