Characterizing Linear Alignment Across Language Models
Language models increasingly appear to learn similar representations, despite differences in training objectives, architectures, and data modalities. This emerging compatibility between independently trained models introduces new opportunities for cross-model alignment to downstream objectives. Moreover, this capability unlocks new potential application domains, such as settings where security, privacy, or competitive constraints prohibit direct data or model sharing. In this work, we investigat
Record details
Published: 19 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Privacy · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (19 March 2026), “Characterizing Linear Alignment Across Language Models,” evidence record 7007, https://ethics.ai/record/7007 (originally published by arXiv).
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