This framework demonstrates improved accuracy in decision-making via structured debate in diverse AI models, suggesting enhanced reasoning capabilities.
Key Points
The aim is to develop a deliberation system that improves decision-making accuracy using multi-agent interactions and reinforcement learning.
Developed ARIA, a multi-agent deliberation system using six different large language models.
Implemented a three-round deliberation protocol including reconnaissance, analysis, and synthesis phases.
Used a three-loop learning architecture that integrates persona-level reinforcement learning and human-curated skill injection.
Achieved 92.4% accuracy on the GPQA Diamond benchmark, outperforming other models by 4.0 percentage points.
Improved answers on 21 questions where other models failed, demonstrating added reasoning value from synthesis.
Produced calibrated conviction scores with 73.5% accuracy at high conviction and 35.7% at low conviction.