LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misaligned behaviors. We propose Explicit Trait Inference (ETI), a psychologically grounded method for improving coordination. ETI enables agents to infer and track partner characteristics along two established psychological dimensions--warmth (e.g., trust) and competence (e.g., skill)--from interaction histories to guide decisions. We evaluate ETI in controlled settings (economic games), where it reduces payoff loss by 45-77%, and in more realistic, complex multi-agent settings (MultiAgentBench), where it improves performance by 3-29% depending on the scenario and model, relative to a CoT baseline. Additional analysis shows that gains are closely linked to trait inference: ETI profiles predict agents' actions, and informative profiles drive improvements. These results highlight ETI as a lightweight and robust mechanism for improving coordination in diverse multi-agent settings, and provide the first systematic evidence that LLM agents can (i) reliably infer others' traits from interaction histories and (ii) leverage structured awareness of others' traits for coordination.

翻译：基于大语言模型的多智能体系统（MAS）在复杂任务中展现出潜力，但仍易出现协调失灵问题，如目标漂移、错误级联及行为失配。我们提出显式特质推理（ETI）方法，这是一种基于心理学原理的协调优化机制。ETI使智能体能够从交互历史中推断并追踪伙伴特征——沿"温暖度"（如信任）与"能力度"（如技能）两个成熟心理学维度——从而引导决策。我们在受控环境（经济博弈）中评估ETI，该方案能减少45-77%的收益损失；在更真实的复杂多智能体场景（MultiAgentBench）中，相较于思维链基线，性能提升幅度达3-29%（因场景与模型而异）。附加分析表明，性能增益与特质推理紧密相关：ETI特征剖面可预测智能体行为，信息量丰富的剖面推动性能改进。这些结果凸显ETI作为轻量级鲁棒机制，可提升多样化多智能体场景的协调能力，并首次系统性证明：大语言模型智能体（i）能从交互历史中可靠推断他人特质，（ii）能利用对他者特质的结构化认知促进协调。