Sybil-Resistant Service Discovery for Agent Economies

TL;DR

The paper tackles the challenge of discovering trustworthy paid services for autonomous agents within the x402 ecosystem. It introduces TraceRank, a reputation-propagation method that treats each payment as an endorsement and computes a steady-state score $\mathbf{r} = (I - \alpha W^{\top})^{-1} \mathbf{s}$ by diffusing seed reputations through value- and time-weighted payment flows. TraceRank is then fused with semantic vector retrieval using $score(A,q) = cos(q,p_A) * TraceRank(A)$ to deliver high-quality, query-driven results, while resisting Sybil and spam effects. The authors provide practical implementation guidance (eg, SQL, pgvector) and argue that this approach enables fast, scalable, and less infrastructure-biased service discovery for agent economies, outperforming purely volume-based or purely semantic baselines.

Abstract

x402 enables Hypertext Transfer Protocol (HTTP) services like application programming interfaces (APIs), data feeds, and inference providers to accept cryptocurrency payments for access. As agents increasingly consume these services, discovery becomes critical: which swap interface should an agent trust? Which data provider is the most reliable? We introduce TraceRank, a reputation-weighted ranking algorithm where payment transactions serve as endorsements. TraceRank seeds addresses with precomputed reputation metrics and propagates reputation through payment flows weighted by transaction value and temporal recency. Applied to x402's payment graph, this surfaces services preferred by high-reputation users rather than those with high transaction volume. Our system combines TraceRank with semantic search to respond to natural language queries with high quality results. We argue that reputation propagation resists Sybil attacks by making spam services with many low-reputation payers rank below legitimate services with few high-reputation payers. Ultimately, we aim to construct a search method for x402 enabled services that avoids infrastructure bias and has better performance than purely volume based or semantic methods.

Figures (1)

• Figure 1: Reputation propagation: high-seed payers diffuse reputation along value- and time-weighted payment flows to services.