Multimedia verification requires not only accurate conclusions but also transparent and contestable reasoning. We propose a contestable multi-agent framework that integrates multimodal large language models, external verification tools, and arena-based quantitative bipolar argumentation (A-QBAF) as a submission to the ICMR 2026 Grand Challenge on Multimedia Verification. Our method decomposes each case into claim-centered sections, retrieves targeted evidence, and converts evidence into structured support and attack arguments with provenance and strength scores. These arguments are resolved through small local argument graphs with selective clash resolution and uncertainty-aware escalation. The resulting system generates section-wise verification reports that are transparent, editable, and computationally practical for real-world multimedia verification. Our implementation is public at: https://github.com/Analytics-Everywhere-Lab/MV2026_the_liems.

翻译：多媒体验证不仅要求准确的结论，更需具备透明且可争议的推理过程。我们提出一个可争议的多智能体框架，该框架整合多模态大语言模型、外部验证工具以及基于竞技场的量化双极论证系统（A-QBAF），作为ICMR 2026多媒体验证大挑战的参赛方案。本方法将每个案例分解为以声明为中心的段落，检索针对性证据，并将证据转化为包含来源和强度分数的结构化支持与攻击论证。这些论证通过小型局部论证图、选择性冲突消解及不确定性感知升级机制进行解析。最终系统生成逐段验证报告，具有透明、可编辑且计算高效的特性，适用于真实世界的多媒体验证任务。实现代码已公开于：https://github.com/Analytics-Everywhere-Lab/MV2026_the_liems