让AI像人一样跳跃式思考，应对不确定的突发情况。
Thinking Fast and Laterally: Multi-Agentic Approach for Reasoning about Uncertain Emerging Events
- 多智能体协作模拟人类横向思维，动态沟通增强推理。
- 在流式数据中处理复杂低具体性问题，表现优于单智能体。
- 适合需要快速预判和因果推断的实时决策场景。
本文将横向思维引入AI系统，实现不确定性下前瞻性和因果推理的System-2能力。提出一套系统生成与建模横向思维问题的框架及评估数据集。设计Streaming Agentic Lateral Thinking（SALT）多智能体框架，用于在流式数据环境中处理复杂、低具体性查询。SALT通过专用智能体间的动态通信结构，实现受横向思维启发的System-2推理。核心洞察在于：长距离智能体间的信息流动结合细粒度信念管理，可构建更丰富的信息上下文并提升推理能力。初步的定量与定性评估表明，SALT在流式环境中的复杂横向推理任务上具有超越单智能体系统的潜力。
原文摘要 · Abstract (English)
This paper introduces lateral thinking to implement System-2 reasoning capabilities in AI systems, focusing on anticipatory and causal reasoning under uncertainty. We present a framework for systematic generation and modeling of lateral thinking queries and evaluation datasets. We introduce Streaming Agentic Lateral Thinking (SALT), a multi-agent framework designed to process complex, low-specificity queries in streaming data environments. SALT implements lateral thinking-inspired System-2 reasoning through a dynamic communication structure between specialized agents. Our key insight is that lateral information flow across long-distance agent interactions, combined with fine-grained belief management, yields richer information contexts and enhanced reasoning. Preliminary quantitative and qualitative evaluations indicate SALT's potential to outperform single-agent systems in handling complex lateral reasoning tasks in a streaming environment.
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