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
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