# Reward Mining Algorithm – Quality Messaging Incentives

To preserve token value, prevent abuse, and encourage meaningful engagement, Sudo implements a **Proof-of-Message (PoM) Mining System**. This algorithmic, non-inflationary reward engine is designed to **incentivize high-quality user interactions** while filtering out spam and bot activity through strict scoring rules and anti-abuse mechanisms.

#### 1. Proof-of-Message (PoM) Mining Logic

Every time a verified Sudo user sends a message that is successfully delivered, it is evaluated for reward eligibility using weighted scoring factors:

<table data-header-hidden><thead><tr><th width="149"></th><th width="120"></th><th></th></tr></thead><tbody><tr><td><strong>Factor</strong></td><td><strong>Weightage</strong></td><td><strong>Description</strong></td></tr><tr><td>Word Count</td><td>Medium</td><td>Messages between 5–25 words earn maximum points; very short or excessively long spammy messages score lower.</td></tr><tr><td>Engagement</td><td>High</td><td>Messages that are replied to, quoted, or forwarded receive additional reward multipliers.</td></tr><tr><td>Context Type</td><td>Medium</td><td>Group messages receive higher weighting than DMs; channel posts have the highest impact multiplier.</td></tr><tr><td>Session Density</td><td>Low</td><td>Rapid-fire message sending reduces rewards through a diminishing return mechanism.</td></tr><tr><td>Spam Detection</td><td>Critical</td><td>Messages exhibiting bot-like patterns or low content quality are excluded from rewards.</td></tr></tbody></table>

This model prioritizes quality over quantity, ensuring that genuine conversations drive rewards rather than spam volume.

#### 2. Reward Calculation Formula

The PoM reward for each eligible message is determined by:

**R = (B × W × E × C × D) − P**

Where:

* R = Final reward score per message.
* B = Base reward rate (in SUDO) assigned per message type.
* W = Word count factor (0.2 to 1.0).
* E = Engagement multiplier (0.5 to 2.0) based on replies, quotes, and forwards.
* C = Context multiplier (DM = 1.0, Group = 1.2, Channel = 1.5).
* D = Time density factor (0.5 to 1.0), reducing rewards for high-frequency bursts.
* P = Penalty score, applied if spam indicators are detected.

This formula ensures rewards scale based on message quality, engagement value, and contextual impact.

#### 3. Reward Limits by User Tier

To maintain fairness and prevent reward farming, users are segmented into tiers with daily caps:

| **Tier**      | **Max Daily Messages** | **Max Daily Reward (SUDO)** |
| ------------- | ---------------------- | --------------------------- |
| Free Users    | 200                    | 50                          |
| Power Users   | 500                    | 150                         |
| Verified Bots | 1000                   | 200                         |

Rewards are drawn from the pre-allocated mining pool of 40 million SUDO tokens. Once the pool depletes, the reward-per-message value will gradually decline to ensure sustainability.

#### 4. Anti-Abuse Mechanisms

The PoM system employs multiple protective measures to ensure rewards benefit genuine users:

* Wallet Reputation Scoring: On-chain and in-app behavior builds a trust score that impacts reward eligibility.
* AI Content Scoring: Messages are analyzed for quality, originality, and coherence, filtering out gibberish or repetitive spam.
* Web3 Signature Verification: All senders must be verified through cryptographic signatures; optional KYW (Know Your Wallet) verification adds an additional trust layer.
* Automated Blacklisting: Repeat offenders or detected bot accounts are permanently excluded from rewards.

#### 5. Economic Goal

The primary objective of the PoM mining model is:

*Reward messaging as a value-generating behavior without flooding the economy with free tokens.*

By applying dynamic scoring, reward caps, and token burn-backed funding, the system ensures that genuine, high-quality conversations are consistently rewarded, while spam and manipulation are algorithmically and economically disincentivized.

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