zkML / Verifiability

To combat black-box AI concerns, ARP supports zk-verifiable scoring modules:

• Validator score updates signed using zkML outputs

• Reproducible inputs = fully auditable decision-making

• Optionally required for validators above quorum thresholds

This enables transparent AI governance, where LLMs and agents can be audited by chain logic.

---

📊 Validator Score Formula (Abstract)

Let RS be Reputation Score.

RS = f(TWTH, GP, EQ, RQ, SA, CTI) - P

where:
TWTH = Time-Weighted Token Holding
GP   = Governance Participation
EQ   = Engagement Quality
RQ   = Referral Quality
SA   = Sentiment Alignment
CTI  = Community Trust Indicators
P    = Penalties (Sybil, Spam, Malice)

AI agents dynamically evaluate EQ, SA, and CTI using embeddings, historical memory, and behavior tags.

---

📡 Validator Monitoring Tools (Coming Soon)

• Validator Reputation Dashboards

• Governance Alignment Heatmaps

• Public Rep Explorer

• AI Agent Audit Logs

These will be published to explorer.socialblock.io and accessible via API/SDK.

---

📍 ARP in Action: An Example

Validator A:

• Stakes 10,000 $SBLK

• Votes on every proposal

• Runs a chatroom for their DAO

• Has positive endorsements

• Maintains a high signal/noise ratio

Result: Reputation Score = 0.94 Status: Priority validator, top proposer, high APY

Validator B:

• Stakes 25,000 $SBLK

• Misses votes

• Posts repetitive promotional content

• Uses bot-driven referral traffic

Result: Reputation Score = 0.47 Status: Lower block priority, rep-slashing initiated, flagged for audit