A safety direction estimated in a source LLM is transported to a target generator through lightweight alignment on benign data alone, matching native safety performance without any target-side unsafe data.
Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models.arXiv preprint arXiv:2506.00653, 2025
3 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
HyperTransport amortizes activation steering for T2I models via a hypernetwork that predicts intervention parameters from CLIP embeddings, delivering 3600-7000x speedup and matching per-concept baselines on 167 unseen concepts.
The Master Key Hypothesis states that capabilities are low-dimensional directions transferable across models through linear subspace alignment, with UNLOCK demonstrating gains such as 12.1% accuracy improvement on MATH when transferring CoT from 14B to 7B models.
citing papers explorer
-
Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation
A safety direction estimated in a source LLM is transported to a target generator through lightweight alignment on benign data alone, matching native safety performance without any target-side unsafe data.
-
HyperTransport: Amortized Conditioning of T2I Generative Models
HyperTransport amortizes activation steering for T2I models via a hypernetwork that predicts intervention parameters from CLIP embeddings, delivering 3600-7000x speedup and matching per-concept baselines on 167 unseen concepts.
-
The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment
The Master Key Hypothesis states that capabilities are low-dimensional directions transferable across models through linear subspace alignment, with UNLOCK demonstrating gains such as 12.1% accuracy improvement on MATH when transferring CoT from 14B to 7B models.