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LLM agents can appear to reason well (high entropy) while completely ignoring the input, and mutual information is a far better metric for catching this failure.
LLMs can be steered more effectively by viewing activation manipulation through the lens of ordinary differential equations and control theory, leading to significant gains in alignment benchmarks.
