AI Research
Geometry-Guided Constraint Learning for LLM Safety Classification
Safety as Polytope (SaP) learns linear half-space constraints in LLM hidden space but requires per-category tuning of the constraint count K. We show that sparse autoencoder (SAE) feature extraction resolves this: K=2 becomes optimal for 12/14 categories on Qwen3.5-9B, achieving ...
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ArXiv CS.AI
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ArXiv CS.AI
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This research proposes a significant simplification for LLM safety classification by leveraging sparse autoencoders to identify consistent, low-dimensional linear boundaries. This geometric approach dramatically reduces the computational burden of tuning classification parameters, making robust safety verification more accessible.
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