The software engineering research community faces a systemic crisis: peer review is failing under growing submissions, misaligned incentives, and reviewer fatigue. Community surveys reveal that researchers perceive the process as "broken." This position paper argues that these dysfunctions are mechanism design failures amenable to computational solutions. We propose modeling the research community as a stochastic multi-agent system and applying multi-agent reinforcement learning to design incentive-compatible protocols. We outline three interventions: a credit-based submission economy, MARL-optimized reviewer assignment, and hybrid verification of review consistency. We present threat models, equity considerations, and phased pilot metrics. This vision charts a research agenda toward sustainable peer review.

翻译：软件工程研究界正面临系统性危机：随着投稿量激增、激励错配及评审者疲劳加剧，同行评审体系正趋于失效。社群调查显示，研究者普遍认为当前流程已“崩溃”。本立场论文指出，这些功能障碍本质上是机制设计失效，可通过计算方案予以解决。我们提出将研究社群建模为随机多智能体系统，并应用多智能体强化学习来设计激励相容协议。本文规划了三项干预措施：基于信用的投稿经济体系、MARL优化的评审人分配机制，以及评审一致性的混合验证框架。我们同时阐述了威胁模型、公平性考量及分阶段试点评估指标。这一愿景为构建可持续的同行评审体系规划了研究路径。