Activation inversion metamodels scale interpretation of residual stream directions
MAEMs generate text exemplars that activate supplied residual stream directions better than corpus search or autoencoders.
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MAEMs generate text exemplars that activate supplied residual stream directions better than corpus search or autoencoders.
LexFrame[1] reaches 0.97 coherence on SmolLM3-3B and OLMo3-7B, versus 0.30 and 0.35 for fixed lenses.
U-Space maps language-model residual states into a subspace that tracks token-level doubt and certainty without extra training.
Six language models show residual streams become geometrically specific to their own final states during inference.
A residual-stream covert-channel attack hides sensitive information in LLM intermediate activations, recoverable with a simple linear decoder without retraining or weight modification.
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