TrustFlow: Topic-Aware Vector Reputation Propagation for Multi-Agent Ecosystems

Abstract

We introduce TrustFlow, a reputation propagation algorithm that assigns each software agent a multi-dimensional reputation vector rather than a scalar score. Reputation is propagated through an interaction graph via topic-gated transfer operators that modulate each edge by its content embedding, with convergence to a unique fixed point guaranteed by the contraction mapping theorem. We develop a family of Lipschitz-1 transfer operators and composable information-theoretic gates that achieve up to 98% multi-label Precision@5 on dense graphs and 78% on sparse ones. On a benchmark of 50 agents across 8 domains, TrustFlow resists sybil attacks, reputation laundering, and vote rings with at most 4 percentage-point precision impact. Unlike PageRank and Topic-Sensitive PageRank, TrustFlow produces vector reputation that is directly queryable by dot product in the same embedding space as user queries.

Figures (3)

• Figure 1: Convergence of TrustFlow under projection and squared-gating transfer, with and without per-iteration normalization. The log-scale residual decreases linearly, confirming the contraction mapping rate $\alpha = 0.85$. Squared gating converges faster in unnormalized mode due to higher self-alignment.
• Figure 2: P@5 under four adversarial scenarios. The dashed line marks the 78% baseline. All attacks produce ${\leq}4$pp impact; cross-domain sybil and reputation laundering are structurally resisted.
• Figure 3: P@5 scales with interaction data. Moving from 70 to 768 edges improves strict P@5 by 10pp and multi-label P@5 by 10pp. The y-axis begins at 60% to highlight the improvement range.