Self Consistency Agent

Overview

The Self Consistency Agent is a specialized framework designed to significantly boost the reliability and robustness of LLM outputs. Instead of relying on a single model pass, it executes the core task multiple times independently using different samples or reasoning paths. The final output is then synthesized through a majority voting mechanism, ensuring that the most frequently suggested answer emerges as the definitive result.

Capabilities

• Multiple Sampling: Generates num_samples independent responses to mitigate single-point failures in model generation.
• Majority Voting: Implements a consensus mechanism to aggregate diverse outputs into one coherent, statistically supported final answer.
• Configurable Depth: Allows control over the reasoning process via max_loops, enabling deeper self-correction within each sample run.

Example Use Cases

This agent excels in critical decision support systems where ambiguity is costly. For instance:

1. Fact Verification: Asking it to summarize a complex legal document multiple times will yield the most consistently stated key clauses.
2. Code Generation Review: Generating several versions of a function and letting the majority vote select the most robust implementation.
3. Complex Problem Solving: When solving multi-step logic puzzles, running the process five times increases confidence in the final derived solution.