LoRA as Oracle
Quick summary
arXiv:2601.11207v2 Announce Type: replace-cross Abstract: Practitioners increasingly deploy neural networks they did not train, and must audit them after the fact for hidden backdoors, without the training pipeline, the poisoned data, or knowledge of any trigger. We introduce a low-rank auditing lens built on a single observation: what a model has internalized and how it behaves are distinct axes that can diverge. Fitting a small low-rank adapter toward a hypothesis and reading the geometry of the resulting update, its energy relative to, and its alignment with, the frozen weights, measures in
Key takeaways
- arXiv:2601.11207v2 Announce Type: replace-cross Abstract: Practitioners increasingly deploy neural networks they did not train, and must audit them after the fact for hidden backdoors, without the training pipeline, the poisoned data, or knowledge of any trigger.
- We introduce a low-rank auditing lens built on a single observation: what a model has internalized and how it behaves are distinct axes that can diverge.
- Fitting a small low-rank adapter toward a hypothesis and reading the geometry of the resulting update, its energy relative to, and its alignment with, the frozen weights, measures in
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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