CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict

• URL: http://arxiv.org/abs/2605.28369v1
• Date: Wed, 27 May 2026 12:07:44 GMT
• Title: CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict
• Abstract summary: E-commerce platforms have begun recruiting crowdsourced jurors to adjudicate massive volumes of transaction disputes.<n>We introduce a pioneering task, E-commerce Dispute Verdicts (EDV), and present VerdictBench, a multimodal benchmark comprising 6,000 real-world cases.<n>We propose CyberJurors, a multi-agent framework to clarify the dispute logic and regulate the verdict process.
• Score: 59.06704011432714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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