5 papers
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
Xiangchen Cheng, Yunwei Jiang, Jianwen Sun +7
Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to…
GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning
Xiang Cheng, Yulan Hu, Lulu Zheng +3
Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single user, where the field is approachin…
Beyond Itinerary Planning-A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks
Xiang Cheng, Yulan Hu, Xiangwen Zhang +5
Travel planning is a natural real-world task to test large language models' (LLMs) planning and tool-use abilities. Although prior work has studied LLM performance on travel planni…
AMAP Agentic Planning Technical Report
AMAP AI Agent Team, Yulan Hu, Xiangwen Zhang +22
We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and…
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
Xiang Cheng, Chengyan Pan, Minjun Zhao +5
In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce Chain-of-Thought (CoT) to exemplars of ICL to enhance the r…