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

Volodymyr Seliuchenko

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arXiv · 2603.19452

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

Volodymyr Seliuchenko

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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.

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Volodymyr Seliuchenko. 2026-03-19. TrustFlow: Topic-Aware Vector Reputation Propagation for Multi-Agent Ecosystems. https://arxiv.org/abs/2603.19452

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