AI safety and alignment

Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.

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arXiv Computer Vision
Sep 30

OmniTaskonomy: When Does Visual Generation Improve Visual Understanding?

arXiv:2609.38079v1 Announce Type: new Abstract: Training a model to generate visual content can encourage it to learn rich perceptual capabilities related to geometry, spatial relationships, and obje...

By Jiaxin Ge, Yiming Qin, Ji Xie, Haozhe Jiang, Xiaochuang Han, Junyi Zhang, Andrew Dai, Yinfei Yang, Jitendra Malik, Ranjay Krishna, Sewon Min, Haiwen Feng, Le Xue, Baifeng Shi, Trevor Darrell, XuDong Wang
arXiv AI
Sep 30

PILLAR: Private Inverted-Index Lexical Lookup for Augmented Retrieval

arXiv:2609.36326v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) hands the user's query to whoever hosts the corpus. We propose PILLAR, a Privacy-Preserving RAG (PPRAG) system bas...

By Truong Son Nguyen (Arizona State University), Daniel Blackley (George Mason University), Ni Trieu (Arizona State University), Evgenios M. Kornaropoulos (George Mason University)
arXiv AI
Sep 30

The Safety Operator: Modulating the Expression of Safety Instructions via Spectral Optimization

arXiv:2609.36434v1 Announce Type: new Abstract: Context tokens in a transformer-based language model can be absorbed into the model's weights as a multiplicative operator. We study this operator in t...

By Benoit Dherin, Michael Munn, Xavier Gonzalvo, Adrian Goldwaser, Blaz Bratanic, Ananth Balashankar, Andrey Vlasov, Pinzhi Huang, Cecile Loge, Nicole Mitchell, Andre Fernandes, Trilok Acharya, Wendy Kan, Ziyue Wang, Hanna Mazzawi, Felipe Tiengo Ferreira, Mor Geva