让大模型自主搜索知识图谱，零样本实现更精准的时序问答。
Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering
- 大模型自选下一步动作，动态探索知识图谱，无需预设推理流程。
- 在三个基准上分别超越最强基线10.7、4.9、11.2分，达新SOTA。
- 无需训练，可生成可审计的推理轨迹，适合需要透明性的场景。
时序知识图谱问答（TKGQA）因需在复杂时间约束下进行多跳推理而具有挑战性。现有基于大语言模型（LLM）的方法虽提升了语义建模能力，但仍依赖固定推理流程或昂贵的微调，限制了适应性且不利于错误恢复。本文发现，直接赋予现成大模型自主决定下一步行动的能力，在零样本设置下已具有效性。据此提出AT2QA：一种无需训练、具备自主探索能力的问答代理。AT2QA使大模型通过通用搜索工具与时序知识图谱迭代交互，实现动态自我修正。为进一步激发大模型在复杂时序推理中的潜力，引入无训练的经验挖掘机制，从成功自生成轨迹中提炼出紧凑的少样本示范库。此外，该方法为每次预测生成可解释的审计轨迹。在MultiTQ、Timeline-CronQuestion和Timeline-ICEWS-Actor三个挑战性基准上的实验表明，AT2QA分别超越最强基线10.7、4.9和11.2个百分点，达到新最优性能。代码已开源。
原文摘要 · Abstract (English)
Temporal Knowledge Graph Question Answering (TKGQA) is challenging because it requires multi-hop reasoning under complex temporal constraints. Recent LLM-based approaches have improved semantic modeling for this task, but many still rely on fixed reasoning workflows or costly post-training, which can limit adaptability and make error recovery difficult. We show that enabling an off-the-shelf Large Language Model (LLM) to determine its next action is already effective in a zero-shot setting. Based on this insight, we propose AT2QA, an Autonomous and Training-free Agent for TKG Question Answering. AT2QA empowers the LLM to iteratively interact with the TKG via a generic search tool, inherently enabling autonomous exploration and dynamic self-correction during reasoning. To further elicit the LLM's potential for complex temporal reasoning, we introduce a training-free experience mining mechanism that distills a compact few-shot demonstration library from successful self-generated trajectories. AT2QA also yields a transparent audit trail for every prediction. Experiments on three challenging benchmarks -- MultiTQ, Timeline-CronQuestion, and Timeline-ICEWS-Actor -- show that AT2QA achieves new state-of-the-art performance, surpassing the strongest baselines by 10.7, 4.9, and 11.2 absolute points, respectively. Our code is available at https://github.com/AT2QA-Official-Code/AT2QA-Official-Code
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