Autonomous LLM-based agents are increasingly engaging in decentralized service interactions to collaboratively execute complex tasks. However, the intrinsic instability and low-cost generativity of LLMs introduce a systemic vulnerability, where self-interested agents are incentivized to pursue short-term gains through deceptive behaviors. Such strategies can rapidly proliferate within the population and precipitate a systemic trust collapse. To address this, we propose Ev-Trust, a strategy-equilibrium trust mechanism grounded in evolutionary game theory. Ev-Trust constructs a dynamic feedback loop that couples trust evaluation with evolutionary incentives, embedding interaction history and reputation directly into the agent's expected revenue function. This mechanism fundamentally reshapes the revenue structure, converting trustworthiness into a decisive survival advantage that suppresses short-sightedness. We provide a rigorous theoretical foundation based on the Replicator Dynamics, proving the asymptotic stability of Evolutionary Stable Strategies (ESS) that favor cooperation. Experimental results indicate that Ev-Trust effectively eliminates malicious strategies and enhances collective revenue, exhibiting resilience against the invasion of mutant behaviors.

翻译：基于大语言模型（LLM）的自主智能体正越来越多地参与到去中心化的服务交互中，以协作执行复杂任务。然而，LLM固有的不稳定性与低成本的生成能力引入了一种系统性脆弱性，即自利的智能体有动机通过欺骗行为追求短期收益。此类策略可能在群体内迅速扩散，并引发系统性的信任崩溃。为解决这一问题，我们提出了Ev-Trust，一种基于演化博弈论、达到策略均衡的信任机制。Ev-Trust构建了一个将信任评估与演化激励耦合的动态反馈循环，将交互历史与声誉直接嵌入智能体的预期收益函数中。该机制从根本上重塑了收益结构，将可信度转化为抑制短视行为的决定性生存优势。我们基于复制者动力学提供了严格的理论基础，证明了倾向于合作的演化稳定策略（ESS）的渐近稳定性。实验结果表明，Ev-Trust能有效消除恶意策略并提升集体收益，且对突变行为的入侵表现出鲁棒性。