Machine Collective Intelligence for Explainable Scientific Discovery
- URL: http://arxiv.org/abs/2604.27297v1
- Date: Thu, 30 Apr 2026 01:15:54 GMT
- Title: Machine Collective Intelligence for Explainable Scientific Discovery
- Abstract summary: Machine collective intelligence orchestrates multiple reasoning agents to evolve their symbolic hypotheses.<n>Across scientific systems governed by deterministic, or previously uncharacterized dynamics, machine collective intelligence autonomously recovered the underlying governing equations.
- Score: 20.865893351903072
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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