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This work connects Adam loss spikes to both the mismatch between momentum timescales and finite-scale superquadratic loss geometry beyond the Hessian, and shows that confident cross-entropy losses develop a core--wall landscape comprising a narrow quadratic core followed by a steep wall, which produces effective superq...
This work identifies a phenomenon termed token-level memorization asymmetry through theoretical analysis of diffusion training dynamics and proposes Q-Skew, a quantile-weighted skewness-based indicator for membership inference on finetuned DLMs.
The streamlined, replay-free approach proves highly effective on a new continual learning benchmark and a practical path toward building prehensive, continually-learning medical VLMs and advancing the development of medical AI.
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