Advancing Swarm Intelligence
Arboria Labs studies how local rules and decentralized coordination produce robust collective behavior at extreme scale. We publish research and ship open tooling for simulating, measuring, and controlling multi-agent systems from millimeter-scale micro robots to interstellar probe swarms.
Start here
- New to swarm intelligence? Read what it is, then the key principles.
- Here for the research? The papers index is the shortest path.
- Want to run the tools? Start with the Leviathan Engine and a minimal example.
- Collaborating with us? See Team and Contact.
Unifying Swarm Intelligence and Distributed Systems
Swarm intelligence and distributed systems are two halves of one problem: coordinate many agents under local information, bounded communication, and partial failure. We work across both — adaptive flocking and stigmergic coverage on one side, CRDT-based intent and delay-tolerant networking on the other — with measurement-first instrumentation that maps local parameter choices to global regime behavior.
Integrated Arboria Framework
The Leviathan Engine
High-performance C++ simulation core for large swarms. Advances agent states with physics, communication, and fault modeling, and logs structured outputs for orchestration and visualization. Learn more →
Gossamer Threaded Intelligence
Python library of swarm coordination algorithms, communication protocols, and swarm-quality metrics. Plugs into Leviathan for full-system runs or stands alone for prototyping. Learn more →
Maneuver.Map
Orchestrates experiments, tunes Gossamer parameters, drives Leviathan, writes CSV/Parquet frames, and renders a performant 3D view in the browser. Learn more →
Our Focus Areas
Fault-Tolerant Distributed Coordination
We design and measure coordination protocols that survive delay, partition, and agent failure at swarm scale. Our work on CRDT-based intent propagation over contact-plan DTN keeps million-agent swarms coherent across hour-scale light delays and relay attrition.
Planetary and Interstellar Applications
We apply swarm methods to problems that can’t be solved with monolithic systems: distributed interstellar survey, autonomous lunar construction, in-situ resource utilization, and large-baseline sensor networks. These scenarios are the forcing function for our algorithmic and engineering choices.
Our Research Ethics
Autonomous collectives have a specific set of risks that are not addressed by generic AI ethics. We take them seriously:
Dual-use caution. Swarm methods can be used for surveillance, denial, and coercion. We do not contribute directly to lethal autonomous weapons and we gate publications that could materially accelerate them.
Orbital and planetary responsibility. Large deployments in space create Kessler-syndrome risk and planetary contamination risk. Our simulations and recommendations treat debris, deorbit, and forward-contamination as first-order constraints.
Reproducibility over spectacle. Every paper and every tool release lands with seeds, wheel SHAs, and configs so results can be reproduced, critiqued, and corrected.
Open tools, careful claims. Our simulation stack is open. Our performance claims are paired with reproducibility artifacts and explicit limitations, not marketing.
Learn more about our principles →
Latest Updates
Stay informed about our latest breakthroughs, publications, and releases.
Arboria Labs: swarm intelligence and distributed systems research, measurement-first.