Machine Collective Intelligence for Explainable Scientific Discovery

TL;DR

This paper introduces machine collective intelligence, a novel AI paradigm combining symbolism and metaheuristics, enabling autonomous discovery of scientific equations with high interpretability and extrapolation accuracy.

Contribution

It presents a unified framework that orchestrates multiple reasoning agents to autonomously discover governing equations without domain-specific knowledge.

Findings

Recovered underlying equations across various scientific systems.

Reduced extrapolation error by up to six orders of magnitude.

Condensed large models into a few interpretable parameters.

Abstract

Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated substantial capabilities in function approximation, the discovery of explainable and extrapolatable equations remains a fundamental limitation of modern AI, posing a central bottleneck for AI-driven scientific discovery. Here, we present machine collective intelligence, a unified paradigm that integrates two fundamental yet distinct traditions in computational intelligence--symbolism and metaheuristics--to enable autonomous and evolutionary discovery of governing equations. It orchestrates multiple reasoning agents to evolve their symbolic hypotheses through coordinated generation, evaluation, critique, and consolidation, enabling scientific discovery beyond single-agent inference. Across scientific systems governed by deterministic, stochastic, or previously uncharacterized dynamics, machine collective intelligence autonomously recovered the underlying governing equations without relying on hand-crafted domain knowledge. Furthermore, the resulting equations reduced extrapolation error by up to six orders of magnitude relative to deep neural networks, while condensing 0.5-1 million model parameters into just 5-40 interpretable parameters. This study marks an important shift in AI toward the autonomous discovery of principled scientific equations.