FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness
Robust adaptation of LLMs and VLMs is often evaluated by average accuracy or average consistency under perturbations. However, these averages can hide a structured failure mode: a prediction may remain correct while probability mass already flows from particular true classes toward systematic wrong competitors near the decision boundary. In this paper, we formalize this phenomenon as margin-aware error flow and introduce FragileFlow, a plug-in regularizer that uses a calibrated margin buffer to identify correct-but-fragile predictions and organize their off-class probability mass into a class-wise vulnerable-risk matrix. Theoretically, we provide the first PAC-Bayes upper bound for this m...
| Authors | Zhuoyun Li, Boxuan Wang, Jinwei Hu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 May 2026 |
| Last updated at source | 6 September 2026 |
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