LLM-based agents increasingly coordinate decisions in multi-agent systems, often attaching natural-language reasoning to actions. However, reasoning is neither free nor automatically reliable: it incurs computational cost and, without verification, may degenerate into persuasive cheap talk. We introduce Explanatory Equilibrium as a design principle for explanation-aware multi-agent systems and study a regime in which agents exchange structured reasoning artifacts-auditable claims paired with concise text-while receivers apply bounded verification through probabilistic audits under explicit resource constraints. We contribute (i) a minimal mechanism-level exchange-audit model linking audit intensity, misreporting incentives, and reasoning costs, and (ii) empirical evidence from a finance-inspired LLM setting involving a Trader and a Risk Manager. In ambiguous, borderline proposals, auditable artifacts prevent the cost of silence driven by conservative validation under asymmetric information: without structured claims, approval and welfare collapse. By contrast, structured reasoning unlocks coordination while maintaining consistently low bad-approval rates across audit intensities, audit budgets, and incentive regimes. Our results suggest that scalable, safety-preserving coordination in LLM-based multi-agent systems depends not only on audit strength, but more fundamentally on disciplined externalization of reasoning into partially verifiable artifacts.

翻译：基于大语言模型（LLM）的智能体在多智能体系统中日益协同决策，常将自然语言推理附着于行动之上。然而，推理既非免费亦非天然可靠：它产生计算成本，若缺乏验证机制，可能退化为具有说服力的廉价对话。我们提出“解释性均衡”作为面向解释感知的多智能体系统的设计原则，研究智能体交换结构化推理制品（可审计声明搭配简洁文本），而接收方在显式资源约束下通过概率审计实施有界验证的新范式。本文贡献包括：（i）建立连接审计强度、虚假报告动机与推理成本的微观机制层面交换-审计模型；（ii）基于金融场景启发的LLM环境（含交易员与风险管理人员）的实证证据。在含混模糊的边界提案中，可审计制品可防止非对称信息下保守验证导致的沉默成本——若无结构化声明，审批率与社会福利将全面崩塌。相比之下，结构化推理在维持各审计强度、审计预算与激励体制下持续低误批率的同时，实现了高效协调。我们的研究结果表明：基于LLM的多智能体系统中可扩展且维持安全性的协调机制，不仅取决于审计强度，更根本性地依赖于将推理系统化外化为部分可验证制品的能力。