Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
A Language Model’s Guide Through Latent Space
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Refusal in language models is mediated by a single direction in residual stream activations that can be erased to disable safety or added to elicit refusal.
Proposes a fault-tolerance architecture for AI safety by analogizing unreliable AI artifacts to Byzantine nodes and applying consensus mechanisms.
citing papers explorer
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Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
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Refusal in Language Models Is Mediated by a Single Direction
Refusal in language models is mediated by a single direction in residual stream activations that can be erased to disable safety or added to elicit refusal.
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A Byzantine Fault Tolerance Approach towards AI Safety
Proposes a fault-tolerance architecture for AI safety by analogizing unreliable AI artifacts to Byzantine nodes and applying consensus mechanisms.
- Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought