用进化稳定机制防范AI代理欺诈，提升去中心化服务可信度。

Decentralized LLM-based multi-agent service economies face three vulnerabilities that undermine traditional trust mechanisms: reduced cost of fraud, difficulty in evaluating service quality, and instability of service content. These compounding vulnerabilities can trigger population-level trust collapse and the proliferation of short-sighted strategies. We propose Ev-Trust, an evolutionarily stable trust mechanism that addresses these vulnerabilities through three targeted designs: a cross-validation gate leveraging requestor semantic comprehension to assess response validity, a variance-standardized drift measure filtering endogenous stochasticity from genuine behavioral anomalies, and an embedding of trust signals into the expected revenue function that converts trustworthiness into an evolutionary survival advantage. Based on replicator dynamics with a noisy best response micro-foundation, we prove the asymptotic stability of cooperative evolutionarily stable strategies and derive explicit threshold conditions for maintaining cooperative equilibria. We evaluate Ev-Trust through 100-round simulations with at least 100 heterogeneous LLM-driven agents covering seven behavioral types. The experiments are conducted on TruthfulQA and TriviaQA, two factual question-answering benchmarks. Compared to baselines based on transitive trust aggregation, reinforcement-learning reputation, and pure evolutionary imitation, Ev-Trust reduces malicious agent participation by approximately 60%, suppresses the fraudulent service rate by approximately 50%, and maintains stable trust differentiation under a 30% adversarial mutation. These results demonstrate that coupling semantic trust evaluation with evolutionary incentives provides a principled foundation for securing cooperation in decentralized LLM-based multi-agent systems.