3 papers
cs.LG2026
Size Doesn't Matter: Cosine-Scored Sparse Autoencoders
Silen Naihin, Lev Stambler
Sparse autoencoders (SAEs) detect features via inner product, so a feature's activation scales with both its directional alignment and the input's norm. Features that fire on token…
cs.LG2026
CLaaS: Continual learning as a service for sample efficient online learning
Kion Fallah, Silen Naihin, Barak Widawsky +1
Deployed large language model agents must adapt to distribution shift in dynamic environments. Ideally, adaptation can be performed from accumulated agent experiences and retain pr…
cs.AI2023
Testing Language Model Agents Safely in the Wild
Silen Naihin, David Atkinson, Marc Green +5
A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet real-world autonomous tests face several unique safety challenges, both due to the possibility of caus…