多智能体推理用于说话人关系推断
原标题：Who Are They to Each Other? Multi-Agent Reasoning for Speaker Relationship Inference
AI 摘要
该论文提出一种无需训练的多智能体推理框架，用于从口语对话中推断说话人之间的关系。框架包含两种设计：多角色多智能体辩论（MRMAD）为智能体分配互补角色或社会理论视角；多智能体竞争（MAC）通过成对裁决比较假设并淘汰较弱候选。在 Seamless Interaction 数据集上的实验表明，这些方法在多数情况下优于零样本和现有多智能体基线，但声学线索仍未被当前模型充分捕捉。
正文节选
Who Are They to Each Other? Multi-Agent Reasoning for Speaker Relationship Inference Abstract Inferring speaker relationships from spoken conversations is an important step towards socially aware speech understanding. However, this task remains underexplored, and supervised modeling is costly to train and scale. At the same time, existing inference-time LLM approaches provide limited structure for handling subtle, distributed, and multimodal relational cues that may support multiple plausible in