AI-driven, anti-Sybil reputation scoring that converts raw on-chain behavior into a bot-likelihood signal and risk tiers (Clean / Monitor / High-Risk). It blends temporal patterns, transaction-graph structure, action diversity, and economic rationality to help gate rewards and access before budgets get drained by bots.
Temporal patterns: activity rhythm, streaks vs. bursts, synchronicity with campaign “waves.”
Transaction graph: connectivity density, cyclic routes, fan-out/fan-in, shared counterparties.
Action diversity: share of unique contracts, depth of sequences (e.g., bridge → deposit → swap → LP → vote).
Economic sense: fee/volume balance, net inflows/outflows, asset retention vs. instant withdrawals.
Cross-chain signals: recurring routes and migrations across networks.
A combination of gradient-boosting and sequence/graph models produces a Bot Likelihood score (0–1) plus explanatory flags (e.g., sync-batch, cycle-flow, fee-volume-outlier).
Clean — low risk
Monitor — medium risk
High-Risk — high bot probability
For integrations, we return a brief explanation of which features pushed the score up/down.
Use risk tiers together with MRS / PoH / Score Level:
Example rule: allow access if High-Risk = false AND MRS ≥ 500 AND Level ≥ 6.
Tighten thresholds for airdrops; relax them for onboarding flows.
Appeals and manual samples improve quality over time: we tune thresholds per campaign/ecosystem and update features/weights based on outcomes.
Combine AI risk with reputation metrics (MRS / PoH / Levels) to reduce false positives/negatives.
Set per-campaign thresholds (stricter for airdrops, softer for onboarding).
For Monitor tier, prefer soft limits (reduced caps) over hard bans.
The AI anti-bot layer turns raw transactions into a clear risk signal, enabling informed gating and rewards decisions before bots consume your campaign budget.
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