AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work studies activations from ensembles of prompts that require inferring relationships between three tokens corresponding to a cyclic concept to correctly predict the next token and finds a consistent layerwise progression in how the joint dependence among the tokens is geometrically organized and causally used.
The direction of ignorance is causally active: raising or lowering $\lambda$ at the final prediction state steers the prediction toward or away from the unigram prior in KL divergence, with larger models generally exhibiting lower prior reliance in the high-context limit.
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