> For the complete documentation index, see [llms.txt](https://artisan-ai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://artisan-ai.gitbook.io/docs/designing-the-creator-protocol-architecture-and-interfaces/reward-distribution-and-discovery.md).

# Reward Distribution & Discovery

*Creation earns, discovery scales it.*

ArtisanAI is designed to ensure that every meaningful contribution, whether it originates from content creation, model development, or social curation, is quantifiably rewarded and algorithmically surfaced. The protocol establishes a multi-dimensional incentive loop, anchored in verifiable on-chain actions and tied directly to the $ART token distribution.

#### Behavior-Driven Reward Allocation

$ART tokens are distributed continuously and programmatically, based on measurable network participation. Rewarded behaviors include:

* Publishing original content (text, images, video, etc.)
* Deploying and sharing AI models
* Remixing and forking existing works
* Engaging with content (likes, shares, comments)
* Curating or boosting assets through DAO-based mechanisms
* Driving downstream engagement via syndication

Each behavior carries a weighted score calculated via protocol-defined logic. Higher scores correspond to larger share allocations from the reward pool.

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#### Reputation and Signal Quality

To prevent manipulation and ensure long-term value alignment, ArtisanAI incorporates:

* Reputation-weighted impact — accounts with longer, consistent activity have higher influence
* Sybil-resistance heuristics — such as wallet age, DAO participation, and staking history
* Interaction lineage — reward shares flow backward through remix trees to original contributors

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#### Discovery Mechanisms

The protocol includes a discovery layer that functions as a recommendation engine, driven entirely by on-chain activity signals. This ensures that high-quality or innovative work gains visibility — not through centralized editorial control, but through provable interaction trails:<br>

* Trending models & content surfaced by recent engagement metrics
* Remix graph analytics for surfacing influential assets
* Curation staking allowing DAOs or users to boost visibility
* Personalized feeds generated by wallet-linked interaction history

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#### Reflexive Incentive Loop

The interaction between rewards and discovery forms a closed loop:

Creation → Engagement → Visibility → Token rewards → Further creation

This reflexive cycle drives protocol activity, encourages positive feedback behaviors, and supports a self-sustaining creator economy at scale.

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