# Contribution Mechanisms

The ecosystem incentivizes high-value contributions across three main roles, each with distinct integration and reward logic:

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**Data Providers**

Structured data sources can be contributed to improve model training and real-time analytics. Examples include:

• On-chain data streams

• Enriched protocol metadata

• Market and sentiment APIs

Providers are compensated based on the utility and frequency of their data in agent executions and synthesis processes.

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**Evaluators**

Human and automated evaluators assess the quality and performance of models and agent responses. Their tasks include:

• Ranking agent outputs

• Validating factual accuracy and coherence

• Stress-testing domain-specific queries

Evaluators earn rewards proportional to their contribution volume, consistency, and alignment with aggregate user feedback.

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**Intelligence Trainers**

Contributors who help “train” the Trendence Mastermind Engine by submitting structured inputs, annotations, and domain-specific insights. Their data enhances the engine’s ability to route tasks, interpret context, and generate accurate, high-signal responses across use cases.

Typical contributions include:

• Identify which AI agents or models perform best for specific tasks to optimize routing and output quality.

• Trade rationale, risk assessment, and strategy annotations

• On-chain behavior tagging and narrative classification

• Reinforcement signals from real-world execution and agent usage

• Interpretation of trading indicators and technical analysis patterns

• Tokenomics trends (emissions, unlocks, velocity shifts) and macro context explanations

• Clarification of performance metrics across DeFi, RWA, and NFT sectors

Contributors are rewarded based on the downstream impact of their input - measured through routing accuracy, response quality improvements, and increased agent adoption.\\
