让AI从答题机器变身为能共同推理的决策伙伴
Collaborative Causal Sensemaking: Closing the Complementarity Gap in Human-AI Decision Support
- 设计新训练环境，鼓励人机协作思考而非单纯输出答案
- 构建共享认知模型，使人机理解一致、目标协同
- 聚焦信任与互补性评估，推动真正高效的人机团队
基于大语言模型的智能体在专家决策支持中日益普及，但在高风险场景下，人机团队尚未稳定超越表现最佳的个体。我们认为，这种互补性差距源于根本性错配：当前智能体被训练为答案生成器，而非实际决策过程中协同建构因果解释、揭示不确定性并动态调整目标的合作伙伴。真正的决策依赖于‘共构因果理解’这一核心能力，而现有训练流程并未明确培养或评估此能力。为此，我们提出‘协同因果推理’（CCS）研究议程，涵盖奖励协作思维的新训练环境、支持共享心智模型的表示方法，以及以信任与互补性为核心的评估体系。该方向将多智能体系统研究从打造类似预言家的答案引擎，转向培育能与人类共同推理因果结构的协作型智能体，从而提升人机团队的整体效能。
原文摘要 · Abstract (English)
LLM-based agents are increasingly deployed for expert decision support, yet human-AI teams in high-stakes settings do not yet reliably outperform the best individual. We argue this complementarity gap reflects a fundamental mismatch: current agents are trained as answer engines, not as partners in the collaborative sensemaking through which experts actually make decisions. Sensemaking (the ability to co-construct causal explanations, surface uncertainties, and adapt goals) is the key capability that current training pipelines do not explicitly develop or evaluate. We propose Collaborative Causal Sensemaking (CCS) as a research agenda to develop this capability from the ground up, spanning new training environments that reward collaborative thinking, representations for shared human-AI mental models, and evaluation centred on trust and complementarity. Taken together, these directions shift MAS research from building oracle-like answer engines to cultivating AI teammates that co-reason with their human partners over the causal structure of shared decisions, advancing the design of effective human-AI teams.
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