NoRA evaluates visual first-person normative reasoning by requiring models to generate actions with fact-reason-action support graphs, revealing current VLMs struggle to bind correct justifications to actions.
Mechanistic analysis reveals a Commit-Abstain Circuit where early commitment signals overpower later abstention corrections, causing hallucinations; training on its activations improves abstention accuracy by 12.2 points.
BatchNorm running statistics artificially inflate unlearning metrics by up to 78 points, which a weight-preserving forward pass reverses without changing weights.
UFO proposes a flow-oriented continual graph learning framework that combats catastrophic forgetting and noisy-label-induced catastrophic remembering via generative replay and instance reliability scoring, outperforming baselines across benchmarks.