# Ev-Trust: An Evolutionarily Stable Trust Mechanism for Decentralized LLM-Based Multi-Agent Service Economies arXiv: https://arxiv.org/abs/2512.16167 ยท HTML: https://mlsanity.com/paper/2512.16167/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. - Builds on 22 of its 53 references that are in the map. - First posted on arXiv 18 Dec 2025. ## Placed in - LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/) ## Builds on (most cited first, 22 of 22) - [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021) - [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017) - [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020) - [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://mlsanity.com/paper/2306.11698/) (2023) - [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023) - [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023) - [Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark](https://mlsanity.com/paper/2304.03279/) (2023) - [Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast](https://mlsanity.com/paper/2402.08567/) (2024) - [Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems](https://mlsanity.com/paper/2410.07283/) (2024) - [Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities](https://mlsanity.com/paper/2407.07791/) (2024) - [PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety](https://mlsanity.com/paper/2401.11880/) (2024) - [TrustAgent: Towards Safe and Trustworthy LLM-based Agents](https://mlsanity.com/paper/2402.01586/) (2024) - [Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models](https://mlsanity.com/paper/2308.01404/) (2023) - [Emergence of Social Norms in Generative Agent Societies: Principles and Architecture](https://mlsanity.com/paper/2403.08251/) (2024) - [BitTensor: A Peer-to-Peer Intelligence Market](https://mlsanity.com/paper/2003.03917/) (2020) - [Learning to Deceive in Multi-Agent Hidden Role Games](https://mlsanity.com/paper/2209.01551/) (2022) - [Robust Communicative Multi-Agent Reinforcement Learning with Active Defense](https://mlsanity.com/paper/2312.11545/) (2023) - [Cautiously-Optimistic Knowledge Sharing for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2312.12095/) (2023) - [Bottom-Up Reputation Promotes Cooperation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2502.01971/) (2025) ## Built on by (most cited first, 3 of 3) - [Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studies](https://mlsanity.com/paper/2606.23075/) (2026) - [SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems](https://mlsanity.com/paper/2609.00595/) (2026) - [When Should Agent Trust Be Conditional? Characterizing and Attacking Skill-Conditional Reputation in Agent Swarms](https://mlsanity.com/paper/2606.14200/) (2026) ## Abstract (arXiv) 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.