# Reward System Owlto Reward Program serves not only as an incentive for our core community members, but also as a strategic cornerstone for co-evolution with our contributors. As community engagement forms the foundation of our ecosystem's prosperity, we are dedicated to establishing a fair value redistribution system through this initiative.\ \ The current Owlto reward ecosystem comprises two core components: the Referral and Commission Program, and the Points Reward System. Within the referral framework, users can expand their community networks through exclusive invitation links. When referred users complete eligible cross-chain transactions, both the referrer and the referee receive proportional commission rewards, establishing a sustainable incentive cycle. Meanwhile, the Points System employs a multi-dimensional behavioral incentive framework to quantitatively evaluate users' core interactions within the platform - including cross-chain transaction frequency, volume, and multi-chain engagement depth. Points are accumulated according to predefined rules based on these metrics. These two modules operate independently yet synergistically: the referral mechanism focuses on community growth and immediate incentives, while the points system emphasizes long-term behavioral value accumulation. All reward data is transparently recorded on-chain via smart contracts, ensuring verifiable and trustworthy distribution mechanisms. Through this composite incentive model, we aim to establish a virtuous cycle that simultaneously drives community engagement and sustains long-term ecological value, ensuring every participant's contribution receives appropriate recognition and reward. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.owlto.finance/getting-started/reward-system.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.