Title: Science Earth: Establishing a Planetary Operating System for AI-Driven Scientific Discovery

Abstract

The pursuit of scientific breakthroughs requires a combination of intelligence, persistence, and chance, navigating through immense search spaces. Currently, advanced scientific tools remain fragmented; distinct AI systems are dedicated to specific domains such as biological analysis, clinical reasoning, mathematical derivation, or materials simulation. No pre-configured team can foresee every capability required to address a novel question.

Science Earth addresses this fragmentation by functioning as a planet-scale scientific runtime. In this environment, any capability—whether a simulation cluster, a wet-lab robot, a proof engine, or a single-cell pipeline—can interconnect with any other. Collaboration structures arise organically from the nature of the question itself, rather than being pre-defined.

Central to this architecture is the EACN protocol, which enables capabilities to autonomously discover one another, negotiate task ownership, and resolve conflicts between incompatible evidentiary standards. This process occurs without prior knowledge of which agents will interact, effectively shifting the primary organizing challenge from rigid workflow design to open-ended connectivity.

Two distinct experimental runs validate this approach under structurally different conditions:

1. Trans-Pacific Synchronization Study: In a study focused on higher-order Kuramoto synchronization across the Pacific, agents identified and corrected a closure-ratio assumption within the Ott-Antonsen analytic theory. This theory is known to fail outside the Lorentzian limit. The correction was achieved within thirty minutes.

2. Single-Cell Analysis: An eight-agent run processed the 4.88M-cell Kang 2024 pan-cancer atlas. Over a 64.9-hour period, heterogeneous capabilities coupled together under a single structural external instruction. This collaboration produced three new layers of results and anchored findings against an independent wet-lab study focusing on an adjacent CCR8-TIGIT+ Treg subset.

These cases serve as an initial empirical reading rather than a comprehensive benchmark sweep. They demonstrate that when AI capabilities are truly connectable and coordination emerges directly from the problem at hand, scientific reasoning transforms into a distributed, self-correcting process. This represents a significant step toward scaling AI-native discovery to a global level.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC