⁉️ Is model distillation of frontier models like DeepSeek-R1 and Opus 4.6 traceable to how robust each model's distilled family is 🤔? We find evidence of a completely new phenomenon that we term "Vulnerability Monoculture," where distilled models inherit the security risks of their teacher 😱 🚨 Check out our new preprint where we expose the Systemic Adversarial Robustness (SAR) of open-source frontier models Punchline: We show that models distilled from DeepSeek-R1 and Opus 4.6 are systemically less robust! In some cases, 30- 40% collectively less robust 🚨🚨🚨🚨!!!! 📝Blog: leoelm.github.io/blogs/syste… 📄Paper: arxiv.org/abs/2610.27353 w/ @leo_elm @LSzpruch @matthew_wicker 🧵1/7

🚨 Do you always have multiple rewards but don't know how to steer towards an expensive true reward? Don't fret, friends; we have the paper for you! 📣 Steered Not Stirred: Effective Reward Tilting using Surrogate Reward Mixing, led by the incredible @alec_sargood28 and team The Martini Algorithm 🍸 From 007: everyone now has a license to tilt 🤵 📜 Paper: arxiv.org/abs/2610.25502 🔗 Blog: alecsargood.github.io/martin…

🚨 New Paper Alert, 🎤 Accepted for an oral presentation at the NeurIPS 2026 STODY workshop! 📣 Introducing: Normalizing Flow Maps (NFM): Combining few-step transport from Flow maps with tractable likelihoods from Normalizing Flows. This is my first work with my first crop of new PhD students, @francescoshox and @tim_hitge, who genuinely knocked this project out of the park. 📄 Paper: arxiv.org/abs/2610.22711 📝 Blog: the-bose-lab.github.io/norma… 💻 Code: github.com/francescoshox/Nor… 🧵 1/8