Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents

Key points

The study simulated around 10,000 communities of agents across objective mathematics questions and subjective political statements, and released trajectory data at both the individual and group levels.
Collective dynamics collapse into three regimes: indifference, polarization, and consensus. Interaction builds conviction and moves communities toward more ordered states.
On objective questions, communication improves collective accuracy: initially incorrect majorities switch to the correct answer more often than initially correct majorities switch to an incorrect answer. On subjective questions, systematic ideological drift can emerge, including movement toward the political right.
An extension of the Ising model, with intrinsic-field and interaction terms, predicts individual trajectories from initial opinions alone, outperforms all standard baselines, and generalizes to unseen community graphs.
The fitted parameters explain the observations: communities operate below the critical temperature, consistent with conviction buildup; attractive ties are stronger than repulsive ties, favoring consensus; and agents holding the correct answer exert the strongest pull, driving truth-seeking.

External research summaries. These are not HDATF publications or measured product results.