# CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict Source: [CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict]() Collection index: [llms.txt]() Data updated: 2026-05-29T05:00:12.873Z Authors: Yanhui Sun, Wu Liu, Haifeng Ming, Xinru Wang, Hantao Yao, Yongdong Zhang Published: 2026-05-27T12:07:44.000Z ## Abstract E-commerce platforms have begun recruiting crowdsourced jurors to adjudicate massive volumes of transaction disputes. Unlike formal legal judgment, E-commerce dispute verdicts require grounding pivotal clues from redundant, multi-round, multimodal evidence and making decisions under flexible platform-specific conventions. These characteristics render existing methods insufficient for this scenario. To bridge this gap, we introduce a pioneering task, E-commerce Dispute Verdicts (EDV), and present VerdictBench, a multimodal benchmark comprising 6,000 real-world cases designed to reflect crowdsourced jury decisions. Building upon this, we propose CyberJurors, a multi-agent framework to clarify the dispute logic and regulate the verdict process. At the individual level, Individual Verdict Chain-of-Thought decomposes the EDV task into four structured reasoning stages, enabling fine-grained clue perception and clarifying causal logic between pivotal clues and the dispute focus. At the collective level, Jury Consensus Verdict simulates multi-round discussion and voting among jurors, while incorporating verdict precedents to mitigate cognitive biases toward either disputant. Experiments on VerdictBench show that CyberJurors outperforms state-of-the-art LLMs, MLLMs, and court simulators, while achieving stronger alignment with real-world jury voting patterns. Code and dataset are available at https://github.com/YanhuiS/CyberJurors and https://huggingface.co/datasets/piggi/VerdictBench. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [E-commerce dispute verdict prediction on VerdictBench (test)](): E-commerce dispute verdict prediction; VerdictBench (test); Accuracy: 72.92. - [E-Commerce Disputes Verdict on CyberJurors 1.0 (test)](): E-Commerce Disputes Verdict; CyberJurors 1.0 (test); Accuracy: 72.92. ## Original sources and implementation [Original paper]() [PDF]()