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.

翻译：去中心化LLM多智能体服务经济面临三类削弱传统信任机制的脆弱性：欺诈成本降低、服务质量评估困难以及服务内容不稳定。这些叠加的脆弱性可能引发群体层面的信任崩溃与短视策略的蔓延。我们提出Ev-Trust，一种演化稳定的信任机制，通过三项针对性设计应对这些脆弱性：利用请求方语义理解能力评估响应有效性的交叉验证门控、过滤内源性随机波动与真实行为异常的方差标准化漂移度量，以及将信任信号嵌入预期收益函数以将可信度转化为演化生存优势。基于含噪声最优反应微观基础的复制动态模型，我们证明了合作演化稳定策略的渐近稳定性，并推导出维持合作均衡的显式阈值条件。我们通过100轮仿真（每轮包含至少100个涵盖七种行为类型的异构LLM驱动智能体）评估Ev-Trust。实验在TruthfulQA与TriviaQA两个事实性问答基准上进行。与基于传递性信任聚合、强化学习声誉及纯演化模仿的基线方法相比，Ev-Trust使恶意智能体参与率降低约60%，欺诈服务率抑制约50%，并在30%对抗性变异条件下维持稳定的信任分化。这些结果表明，将语义信任评估与演化激励相结合，为保障去中心化LLM多智能体系统中的合作提供了理论化基础。