Evaluate KGFT on broader downstream tasks

Investigate whether Kernel-Guided Feature Transform (KGFT) provides consistent benefits beyond image classification when applied to object detection, semantic segmentation, and parameter-efficient fine-tuning tasks.

Background

Kernel-Guided Feature Transform (KGFT) is evaluated in the paper primarily on image classification with ResNet and ViT architectures and on arithmetic reasoning with LLaMA-7B. The authors state that further evaluation is needed on additional downstream tasks, specifically object detection, semantic segmentation, and parameter-efficient fine-tuning, to determine whether the observed benefits of kernel-guided geometry generalize beyond the settings studied.

References

In future work, we will further explore the application of KGFT to a broader range of downstream tasks, such as object detection, semantic segmentation, and parameter-efficient fine-tuning, to investigate whether kernel-guided geometry can provide consistent benefits beyond classification.

— Dual-Manifold Geometry Guided Representation Learning: Adaptive Coupling between Kernel and Data Spaces  (2608.12737 - Zhang et al., 13 Aug 2026) in Conclusion, Future Work paragraph