TrustFlow: Topic-Aware Vector Reputation Propagation for Multi-Agent Ecosystems
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915876353081344 |
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| author | Seliuchenko, Volodymyr |
| author_facet | Seliuchenko, Volodymyr |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19452 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TrustFlow: Topic-Aware Vector Reputation Propagation for Multi-Agent Ecosystems Seliuchenko, Volodymyr Multiagent Systems Artificial Intelligence I.2.11; H.3.3 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. |
| title | TrustFlow: Topic-Aware Vector Reputation Propagation for Multi-Agent Ecosystems |
| topic | Multiagent Systems Artificial Intelligence I.2.11; H.3.3 |
| url | https://arxiv.org/abs/2603.19452 |