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[2602.15183] Seeing to Generalize: How Visual Data Corrects Binding Shortcuts
Summary
This article explores how Vision Language Models (VLMs) enhance performance on text-only tasks by correcting binding shortcuts through visual training, improving generalization and reasoning abilities.
Why It Matters
Understanding the interplay between visual and textual data in model training is crucial for advancing AI capabilities. This research highlights how integrating visual data can significantly improve the performance of language models, which is relevant for applications in natural language processing and machine learning.
Key Takeaways
- VLMs can outperform LLMs on text-only tasks due to visual training.
- Visual training enhances out-of-distribution performance by changing internal binding strategies.
- Cross-modal training improves reasoning and generalization, even for single-modality tasks.
Computer Science > Machine Learning arXiv:2602.15183 (cs) [Submitted on 16 Feb 2026] Title:Seeing to Generalize: How Visual Data Corrects Binding Shortcuts Authors:Nicolas Buzeta, Felipe del Rio, Cristian Hinostroza, Denis Parra, Hans Lobel, Rodrigo Toro Icarte View a PDF of the paper titled Seeing to Generalize: How Visual Data Corrects Binding Shortcuts, by Nicolas Buzeta and 5 other authors View PDF HTML (experimental) Abstract:Vision Language Models (VLMs) are designed to extend Large Language Models (LLMs) with visual capabilities, yet in this work we observe a surprising phenomenon: VLMs can outperform their underlying LLMs on purely text-only tasks, particularly in long-context information retrieval. To investigate this effect, we build a controlled synthetic retrieval task and find that a transformer trained only on text achieves perfect in-distribution accuracy but fails to generalize out of distribution, while subsequent training on an image-tokenized version of the same task nearly doubles text-only OOD performance. Mechanistic interpretability reveals that visual training changes the model's internal binding strategy: text-only training encourages positional shortcuts, whereas image-based training disrupts them through spatial translation invariance, forcing the model to adopt a more robust symbolic binding mechanism that persists even after text-only examples are reintroduced. We further characterize how binding strategies vary across training regimes, visual ...