Glossary

Term	Definition
DAO	A Decentralized Autonomous Organization (DAO) represented by rules encoded as a computer program that is transparent, controlled by the organization members, and not influenced by a central government.
Large Language Model	AI models trained on vast datasets to understand, generate, and interpret human language, capable of performing a wide range of text-based tasks.
Multi-agent LLM	A system integrating multiple LLMs, each specializing in different tasks or domains, working collaboratively to perform complex operations.
$MODUS Token	The native utility token of the Modus ecosystem, used for transactions, governance, and incentives within the platform.
Tokenomics	The economic and incentive structures within a blockchain-based ecosystem, involving the creation, distribution, and management of tokens.
SmartDAO	A decentralized autonomous organization that harnesses Multi-Agent LLM's to automate governance and operational processes.
SDaaS	A service model that provides tools and platforms to create and manage Smart DAOs, enabling decentralized governance and automated management of multiagent systems.
Token-Based Voting	A voting mechanism in DAOs where the voting power is determined by the number of tokens a participant holds, aligning influence with stake in the organization.
Governance	The system of rules, practices, and processes by which an organization is directed and controlled, often involving stakeholders' participation to make decisions.
Modular System	A flexible architecture where components or systems can be independently created, modified, replaced, or exchanged with other modules or components.
Proprietary Models	Custom-developed models or algorithms that are owned by an organization or individual, typically not shared publicly and used to maintain competitive advantage.
Natural Language Processing	A field of AI focused on enabling computers to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Ensemble Learning	A machine learning technique that combines several base models in order to produce one optimal predictive model, often resulting in better performance than any single constituent model.