
CASE Predicts When Hidden-State Selection Beats Voting
Researchers introduce CASE, a dynamic method that selects LLM answers using a linear correctness signal extracted from hidden states. Its new decodability metric predicts when this approach will outperform majority voting, achieving up to 19-point gains on medium-difficulty questions.




