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While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important concern. One key feature is the ability to refuse or deflect harmful, malicious, or insensitive prompts. In this paper, we expose safety vulnerabilities across six common op...
Contrastive learning is a successful paradigm for learning $d$-dimensional geometric representations from a collection of ``anchor--positive--negative''triplets $(i,j^{+},k^{-})$, indicating that ``item $i$ is closer to $j$ than to $k$.''Despite its success, understanding why contrastive learning leads to representatio...
This work develops tunable latent priors for diffusion models, normalizing flows, and variational autoencoders, leveraging nested dropout and shows empirically that tunable priors consistently achieve lower reconstruction errors than fixed-complexity baselines.
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