✏️ Knowledge Editing¶

📷 CVPR2026 · 2 paper notes

Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors: This paper proposes an attribution-guided dynamic model rectification framework that repositions rank-one model editing from domain adaptation to behavior rectification. By quantifying layer editability via Integrated Gradients to automatically locate suspect layers, it repairs three types of unreliable behaviors—backdoor attacks, spurious correlations, and feature leakage—using only a single clean sample.
SAME: Sparse and Anchored Model Editing for Heterogeneous Incremental Learning under Limited Data: This work adapts the "locate-then-edit FFN key-value pairs" paradigm from Large Language Models (LLMs) to Vision-Language Models (VLMs) like CLIP. Under a newly proposed "Heterogeneous Incremental Learning (HIL)" setting—characterized by no task identities, cross-domain shifts, and few-shot data—the authors propose sparse fine-tuning, dual-anchor constraints, and closed-form solutions to directly "write" new task knowledge into the FFN output projection matrices. The method requires no additional parameters, achieves 6.8% higher average accuracy than existing continual learning methods, and retains 95.8% of oracle performance.