谁能看到什么？用于大语言模型中认知推理的结构化思维-行动序列
Who Sees What? Structured Thought-Action Sequences for Epistemic Reasoning in LLMs
AI总结：
针对基于LLM的智能体在视角采择任务中的挑战，本研究提出基于Fast Downward规划器解图的结构化思维-行动示例生成管道，发现其虽在部分场景有效，但需结合信念跟踪等技术才能实现稳健视角采择。
摘要：
大语言模型（LLMs）和推理框架的最新进展为提升自主智能体的视角采择能力开辟了新可能。然而，涉及主动感知、协作推理和视角采择（理解其他智能体能看到或知道什么）的任务，对当前基于LLM的系统构成持续挑战。本研究探究了从Fast Downward规划器生成的转换解图中提取的结构化示例，在ReAct框架内提升基于LLM的智能体性能的潜力。我们提出结构化解处理管道，生成三类不同示例：最优目标路径（G型）、信息节点路径（E型）和对比替代行动的分步最优决策序列（L型）。通过提示LLM明确阐述每个决策背后的推理，这些解进一步转化为“思维-行动”示例。尽管L型示例略微减少了澄清请求和总体行动步骤，但未带来一致改进。智能体在需基本注意力过滤的任务中表现成功，但在需对遮挡空间进行心理建模或权衡认知行动成本的场景中存在困难。这些发现表明，仅靠结构化示例不足以实现稳健的视角采择，强调需明确的信念跟踪、成本建模和更丰富的环境，以支持基于LLM的智能体开展社会基础上的协作。
英文摘要：
Recent advances in large language models (LLMs) and reasoning frameworks have opened new possibilities for improving the perspective -taking capabilities of autonomous agents. However, tasks that involve active perception, collaborative reasoning, and perspective taking (understanding what another agent can see or knows) pose persistent challenges for current LLM-based systems. This study investigates the potential of structured examples derived from transformed solution graphs generated by the Fast Downward planner to improve the performance of LLM-based agents within a ReAct framework. We propose a structured solution-processing pipeline that generates three distinct categories of examples: optimal goal paths (G-type), informative node paths (E-type), and step-by-step optimal decision sequences contrasting alternative actions (L-type). These solutions are further converted into ``thought-action'' examples by prompting an LLM to explicitly articulate the reasoning behind each decision. While L-type examples slightly reduce clarification requests and overall action steps, they do not yield consistent improvements. Agents are successful in tasks requiring basic attentional filtering but struggle in scenarios that required mentalising about occluded spaces or weighing the costs of epistemic actions. These findings suggest that structured examples alone are insufficient for robust perspective-taking, underscoring the need for explicit belief tracking, cost modelling, and richer environments to enable socially grounded collaboration in LLM-based agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
