You can make confidence-weighted answer selection 47% cheaper by clustering similar reasoning traces and only evaluating unique ones, without sacrificing accuracy.
VecCISC reduces the cost of weighted majority voting for LLM reasoning by filtering out duplicate or low-quality reasoning traces before sending them to a critic model. It uses semantic similarity to identify which candidate answers are worth evaluating, cutting token usage by 47% while maintaining accuracy across math, science, and reasoning tasks.