self-consistency

Multi-path reasoning solves this by sampling diverse reasoning paths and applying majority voting to select the most consistent answer. This approach significantly improves accuracy when single-path reasoning is unreliable for fixed-answer problems.

Self-consistency voting generates multiple reasoning paths for logic puzzles and selects the final answer via majority vote. Use it for tasks with fixed answers or high ambiguity where a single inference path is unreliable.

Configure the Skill to sample a specific number of diverse reasoning paths for your query. It then extracts the final answer from each generated path and applies majority voting to produce the most consistent final result.

Yes, multi-path reasoning with majority voting works effectively for fact-based queries. By comparing multiple sampled paths, it isolates the most consistent answer, improving reliability for factual tasks with fixed answers.

Majority vote for reasoning path selection is limited to tasks with fixed answers or high ambiguity. It is not suitable for open-ended generation tasks where a single correct answer cannot be extracted and compared.