Context is All You Need

2026-04-05

AI Summary

This work addresses the generalization challenge arising from distribution shifts between training and test data by proposing CONTXT, a lightweight test-time adaptation method that modulates intermediate neural representations through additive and multiplicative feature transformations. Designed for minimal computational overhead and broad applicability, CONTXT enables seamless integration into diverse architectures—including convolutional neural networks and large language models—without requiring retraining. The approach enhances model robustness and performance on unseen domains under both domain generalization (DG) and test-time adaptation (TTA) settings, demonstrating consistent gains across discriminative and generative tasks through its simple yet effective context-aware modulation mechanism.

Abstract

Artificial Neural Networks (ANNs) are increasingly deployed across diverse real-world settings, where they must operate under data distributions that differ from those seen during training. This challenge is central to Domain Generalization (DG), which trains models to generalize to unseen domains without target data, and Test-Time Adaptation (TTA), which improves robustness by adapting to unlabeled test data at deployment. Existing approaches to address these challenges are often complex, resource-intensive, and difficult to scale. We introduce CONTXT (Contextual augmentatiOn for Neural feaTure X Transforms), a simple and intuitive method for contextual adaptation. CONTXT modulates internal representations using simple additive and multiplicative feature transforms. Within a TTA setting, it yields consistent gains across discriminative tasks (e.g., ANN/CNN classification) and generative models (e.g., LLMs). The method is lightweight, easy to integrate, and incurs minimal overhead, enabling robust performance under domain shift without added complexity. More broadly, CONTXT provides a compact way to steer information flow and neural processing without retraining.

Problem

• Domain Generalization
• Test-Time Adaptation
• distribution shift
• unseen domains
• model robustness

Innovation

• Test-Time Adaptation
• Domain Generalization
• Feature Modulation
• Contextual Adaptation
• Lightweight Adaptation