让大模型推理过程可解释，自动识别关键论证链条
ARGORA: Orchestrated Argumentation for Causally Grounded LLM Reasoning and Decision Making
- 构建显式论证图，用因果模型追踪观点支持与反驳关系
- 能删减单个论点重算结果，找出决定性推理路径
- 自动修正专家分歧，提升决策正确率并提供诊断依据
现有多专家大模型系统虽收集多元观点，但仅通过简单聚合融合，难以追溯最终决策的依据。本文提出ARGORA框架，将多专家讨论组织为显式的论证图，展示各论点之间的支持或攻击关系。通过将论证图建模为因果网络，可系统性移除特定论点并重新计算结果，识别出哪些推理链是决定性的，并判断在针对性修改下决策是否会改变。我们还引入修正机制，在内部推理与外部判断不一致时进行对齐。在多个基准测试及一个开放式应用场景中，ARGORA达到具有竞争力的准确率，并展现出良好的纠错能力：当专家初始意见分歧时，该框架更倾向于引导至正确答案，而非引入新错误，同时提供对关键论点的因果诊断。
原文摘要 · Abstract (English)
Existing multi-expert LLM systems gather diverse perspectives but combine them through simple aggregation, obscuring which arguments drove the final decision. We introduce ARGORA, a framework that organizes multi-expert discussions into explicit argumentation graphs showing which arguments support or attack each other. By casting these graphs as causal models, ARGORA can systematically remove individual arguments and recompute outcomes, identifying which reasoning chains were necessary and whether decisions would change under targeted modifications. We further introduce a correction mechanism that aligns internal reasoning with external judgments when they disagree. Across diverse benchmarks and an open-ended use case, ARGORA achieves competitive accuracy and demonstrates corrective behavior: when experts initially disagree, the framework resolves disputes toward correct answers more often than it introduces new errors, while providing causal diagnostics of decisive arguments.
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