arXiv AI
Aug 20

ORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery

ORBITER is a new framework for next‑order decision‑making in last‑mile delivery that uses large language models (LLMs) to reason about courier spatiotemporal states and visible orders. It structures decision points with local trade‑offs, ranks candidates via fixed proposers, and employs a critic to verify decisions against gathered evidence. Experiments on data from four cities show ORBITER outperforms state‑of‑the‑art baselines by up to 9.2% on average.

By Mingzhao Li, Chenxi Liu, Yan Zhao, Hao Miao
arXiv AI
Jun 2

TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents

arXiv:2606. 01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existing benchmarks' limitations: 1) overemphasis on constraint compliance, neglecting multi-dimensional qualities like spatio-temporal cost; 2) datasets lacking real-world authenticity and coverage in key areas (e.

By Weiyi Chen, Shuaixiong Wang, Ziyun Gao, Kaichun Hu, Wangze Ni, Shimin Di, Chen Jason Zhang, Lei Chen