AI 中文总结
ExRole是一种多智能体语言模型的轨迹转角色框架，可学习可执行角色，在两个问答基准上显著优于单智能体及其他角色设置，能捕捉可迁移的行为专业化能力。
AI 中文摘要
角色为组织语言模型智能体提供了可解释的接口，但大多数多智能体系统将其视为与学习到的行为和参数更新脱节的手动编写提示标签。我们认为，有用的角色应是可执行的控制变量：它需总结对未来效用具有预测性的行为、指导后续交互，并识别负责该行为的可训练能力。我们提出ExRole，这是一种轨迹转角色框架，它从前缀局部团队轨迹中学习感知未来的角色原型，将其解析为可读指令和与token对齐的角色标记，并可选地通过轮次对齐的信用分配路由共享LoRA秩槽。在MuSiQue和2WikiMultiHopQA数据集上，ExRole相比单智能体搜索分别提升了15.0/14.4和13.5/16.1的EM/F1值；与最强的非ExRole对照组相比，相应的提升仍为11.5/11.6和7.7/9.7点。在两个基准测试中，受控结果始终支持轨迹诱导的角色条件化，优于无角色、手动、随机和打乱的替代方案。角色-智能体-轮次干预进一步表明，诱导的角色捕捉了超出固定智能体身份或轮次位置的可迁移行为专业化能力。
英文摘要
Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behavior and parameter updates. We argue that a useful role should instead be an executable control variable: it should summarize behavior predictive of future utility, guide subsequent interaction, and identify the trainable capacity responsible for that behavior. We introduce ExRole, a trajectory-to-role framework that learns future-aware role prototypes from prefix-local team traces, resolves them into readable instructions and token-aligned role markers, and optionally routes shared LoRA rank slots with turn-aligned credit. Across MuSiQue and 2WikiMultiHopQA, ExRole improves over single-agent search by 15.0/14.4 and 13.5/16.1 EM/F1 points, respectively. Against the strongest non-ExRole controls, the corresponding gains remain 11.5/11.6 and 7.7/9.7 points. Across both benchmarks, the controlled results consistently favor trajectory-induced role conditioning over role-free, manual, random, and shuffled alternatives. Role-Agent-Turn interventions further show that the induced roles capture transferable behavioral specialization beyond fixed agent identities or turn positions.
