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Compounding effects in an AI knowledge citation marketplace are measured through four integrated mechanisms: (1) **Citation Receipt Tracking** — every source payment generates an on-chain receipt with SHA-256 evidence hash and content integrity proof, enabling transparent attribution chains; (2) **Re-Citation Scoring** — the Economic Intelligence Dashboard tracks when AI-generated answers (themselves paid citations) are cited again by other agents, creating a compounding multiplier visible in real-time USDC flow analytics; (3) **Knowledge Gap Closure Feedback** — the Autonomous Knowledge Gap Agent identifies underserved topics, funds bounties to fill them, and auto-registers winning answers as new citable sources, closing loops that generate secondary citation demand; (4) **Agent Spend Policy Enforcement** — policies bound per-citation price, relevance thresholds, and deduplication windows, ensuring that compounding citations remain economically rational and don't create circular payment loops. The CitePay Economic Intelligence Dashboard quantifies this directly: a 'compounding score' tracks how many times AI-generated knowledge re-enters the citation economy, measured in USDC flow by category and refreshed every 15 seconds from live Arc Testnet settlement data.