LiveGeo 基准发布：地理智能体在持续演化世界中的可靠性评测

与以往在固定地理状态上推理和规划的评测不同，LiveGeo 要求智能体主动获取任务相关信息、验证此前决策所依据的证据是否仍然有效，并在必要时修订计划。实验结果显示，即便是前沿 LLM 在静态任务上也表现吃力，没有模型能在所有任务上保持一致优势，而静态任务上的强表现并不能保证动态可靠性。

论文进一步分析指出关键瓶颈：智能体难以获取并转化相关证据为有效决策，也难以在环境演化中维持决策正确性。作者同时开源了 GeoWorld 平台，供社区扩展研究能在演化环境中可靠行动的智能体。

LiveGeo: Benchmarking Geographic Agents in Continuously Evolving Worlds

We introduce GeoWorld, a controllable and continuously evolving geographic environment platform for LLM agents, and LiveGeo, a benchmark for evaluating reliable decision-making in real-world geographic environments using GeoWorld. Unlike existing evaluations that largely reason and plan over fixed geographic states, GeoWorld is grounded in real-world geographic structure and allows external world states to evolve along their own temporal processes while preserving verifiable state histories. LiveGeo places geographic tasks within this evolving environment, requiring agents to actively acquire task-relevant information, verify whether the evidence supporting prior decisions remains valid, and revise their plans when necessary. Our results reveal that even frontier LLMs struggle with static tasks, with no model consistently excelling across tasks, while strong static performance does not ensure dynamic reliability. Further analysis identifies a key bottleneck: agents struggle to acquire and translate relevant evidence into valid decisions and maintain their correctness as the world evolves. By releasing LiveGeo together with its GeoWorld platform, we aim to provide the community with a flexible and extensible infrastructure for studying the next generation of agents capable of reliable action in evolving geographic worlds.

本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：LiveGeo、GeoWorld、LLM智能体、基准测试，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Pengcheng Zhou, Xuanyu Liu, Yanchen Yin, Tianjie Ju, Bobo Li, Shengqiong Wu, Hao Fei, Mong-Li Lee, Wynne Hsu），经 AITOP 多维评分管线结构化提炼。