Research at Coloniy

    Advancing the frontier of collective intelligence through multi-agent systems, adaptive learning, and secure AI architectures.

    Multi-Agent Intelligence

    Exploring how autonomous AI agents can collaborate, learn from each other, and make collective decisions that surpass individual capabilities.

    Adaptive Security

    Developing AI systems that continuously evolve to detect emerging threats, predict attack patterns, and protect digital ecosystems in real-time.

    Transparent AI

    Building explainable AI frameworks that make complex decision-making processes understandable, auditable, and trustworthy.

    Recent Publications

    Game-Theoretic MARL for Analyzing and Countering Sophisticated Economic Attacks.

    Coloniy Research Team • 2025

    This research pioneers the use of game-theoretic multi-agent reinforcement learning (MARL) to model and defend against sophisticated economic exploits in decentralized finance (DeFi). Instead of just detecting transactional fraud, we treat the network as a complex economic game. Our AI agents, representing attackers and defenders, compete to find and neutralize strategic vulnerabilities like flash loan exploits and oracle manipulation. By simulating these adversarial interactions, we aim to proactively identify and patch critical security flaws before they can be exploited, building a new generation of resilient and self-defending financial systems.

    GraphSwarm: A Communicating Multi-Agent Reinforcement Learning System for Proactive Detection of Complex DeFi Exploits.

    Coloniy Research Team • 2025

    A swarm of cooperative AI agents, equipped with local graph perception and a learned communication protocol, can detect and attribute sophisticated, multi-step fraud schemes on Ethereum with higher precision and recall than monolithic (single-agent) GNN models, while providing inherent explainability via its communication traces.