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

翻译：时序知识图谱问答（TKGQA）是一项具有挑战性的任务，因为它需要在复杂的时间约束下进行多跳推理。近期基于大语言模型（LLM）的方法虽然提升了该任务的语义建模能力，但许多方法仍依赖于固定的推理工作流或成本高昂的后训练，这限制了其适应性并导致错误恢复困难。我们证明，在零样本设置下，让现成的大语言模型自主决定下一步行动已具有有效性。基于这一发现，我们提出AT2QA——一种面向TKG问答的自主且免训练的智能体。AT2QA使LLM能够通过通用搜索工具与TKG进行迭代交互，从而在推理过程中自然实现自主探索和动态自我纠错。为进一步激发LLM在复杂时间推理中的潜力，我们引入了一种免训练的经验挖掘机制，该机制从成功的自生成轨迹中提炼出紧凑的少样本演示库。此外，AT2QA还为每次预测生成透明的审计轨迹。在三个具有挑战性的基准测试——MultiTQ、Timeline-CronQuestion和Timeline-ICEWS-Actor——上的实验表明，AT2QA取得了新的最优性能，分别超越最强基线10.7、4.9和11.2个绝对百分点。我们的代码开源在https://github.com/AT2QA-Official-Code/AT2QA-Official-Code