AI models that simulate internal debate dramatically improve accuracy on complex tasks

A new study by Google suggests that advanced reasoning models achieve high performance by simulating multi-agent-like debates involving diverse perspectives, personality traits, and domain expertise.

Their experiments demonstrate that this internal debate, which they dub “society of thought,” significantly improves model performance in complex reasoning and planning tasks. The researchers found that leading reasoning models such as DeepSeek-R1 and QwQ-32B, which are trained via reinforcement learning (RL), inherently develop this ability to engage in society of thought conversations without explicit instruction.

These findings offer a roadmap for how developers can build more robust LLM applications and how enterprises can train superior models using their own internal data.

What is society of thought?

The core premise of society of thought is that reasoning models learn to emulate social, multi-agent dialogues to refine their logic. This hypothesis draws on cognitive science, specifically the idea that human reason evolved primarily as a social ...