Fairness & Anti-Abuse

Caps, hourly budgets, liveness checks, and clear rules that protect honest learners.

This fairness and anti-abuse framework is an initial design, subject to change before TGE based on beta feedback and real-world signals.

We put fairness first. The goal is simple, block bots and farms while keeping the experience smooth for real learners. We prevent abuse with layered technical and economic controls, and we back those systems with transparent governance, community oversight, and rapid remediation when problems appear.

Core Protections

One reward per lesson, per user. Daily and weekly earning caps scale by verification tier so payouts are spread fairly. Rewards are budgeted hourly to smooth time-zone pressure and limit rush-hour advantage. Newly minted tokens have short transfer delays adjusted by tier, for example 48 hours for Tier 1 down to 12 hours for Tier 3, to make farming and wash trading harder.

• Enforce one-claim-per-lesson via immutable lesson IDs and on-chain proofs. • Tiered caps are parameterized by verification level and adjustable via governance. • Hourly budgets are computed from epochal emission parameters, and transfer locks are enforced by smart contract timelocks.

Cooldowns and Retries

Assessments allow paid retries, but retries come with cooldowns to stop grinding. Repeated failures trigger extra checks, such as CAPTCHA or quick video verification, and reduce reward multipliers during failure streaks. The intent is to discourage low-effort farming while letting serious learners recover.

• Example rules: 24-hour cooldown after three fails, maximum three retries per exam, progressive multiplier reduction on consecutive fails. • Extra checks are invoked automatically by fraud scores from behavior models.

Privacy by Design

We collect only what is necessary for fairness. Sensitive data, including raw liveness or voice samples, stays off-chain in encrypted vaults. Proofs of completion and token flows run on-chain for auditability. All data is encrypted in transit and at rest, and we comply with applicable laws such as GDPR. No data is sold.

• Voice data retention policy, encryption standards (TLS, AES-256), and key management details. • On-chain entries contain cryptographic hashes or compact proofs, never raw biometric material.

Community Oversight and Governance

Users can file reports in-app, which feed a moderated queue. High-signal reports earn small YAP rewards, but reporting itself is rate-limited and weighted to limit spam. For protocol-level changes like cap adjustments, token holders vote after a 14-day discussion window, keeping major changes community-driven.

Audits and Monitoring

We will run third-party audits prior to launch and use live dashboards after TGE to track claim rates, anomaly scores, and churn. Automated alerts notify the team of suspicious spikes, and treasury tools can step in to stabilize markets when necessary, without advantaging insiders.

• Audit scope recommendations (smart contracts, fraud models, on-chain economics), preferred vendors, and post-audit remediation SLAs. • Dashboard metrics: claims per minute, average reward per wallet, anomaly z-scores, and geographic claim distributions.

Handling Edge Cases

We detect linked accounts with IP patterns and behavioral graphs, and we consider regional adjustments (for example slightly higher caps in underserved regions) through governance. Disputed rewards go into escrow until resolved, and appeals follow a structured workflow to protect both claimants and the system.

• Escrow workflow: freeze, automated evidence collection, moderator review, and final adjudication; maximum escrow hold time and refund rules. • Cross-account detection uses graph heuristics, device fingerprints, and velocity checks, tuned to minimize false positives.

Multiple layers, technical gates, economic costs, community oversight, and transparent governance, make abuse expensive and rare, while keeping the experience friction-free for legitimate learners.