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Real-time price discovery in an AI knowledge citation market works through live competitive auction mechanisms where multiple knowledge sources are scored simultaneously, with prices determined by demand signals and creator payment flows. In this model, autonomous AI agents submit citation queries with spend policies (max price per citation, relevance thresholds, dedup windows). Sources compete in real-time auctions where Claude evaluates relevance and quality; winning sources receive immediate USDC nanopayments via Circle Gateway x402 on Arc Testnet. Price discovery emerges from: (1) live auction leaderboards showing which sources win routing based on quality scores, (2) on-chain settlement receipts creating transparent cost signals, (3) economic intelligence dashboards tracking 24-hour USDC flow by knowledge category, and (4) autonomous gap-detection agents that post bounties for underserved topics, allowing market prices to self-correct toward knowledge scarcity. Each citation generates a SHA-256 receipt hash with content integrity proof, making prices verifiable and tied directly to creator payouts. The system achieves sub-cent nanopayment efficiency through batched EIP-3009 authorizations, eliminating per-transaction gas costs, so marginal citation value can be discovered and priced accurately without economic friction.