arXiv:2608. 08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work.
By Yijun Pan, Yukun Lian, Kunyu Shi, Junbo Li, Hongwei Xue, Sicong Xie, Guannan Zhang, Xiaoying Xing
The paper introduces $ au^ au$-Bench, a benchmark that turns the construction of AI agents into a measurable task. In this environment a developer agent receives real business records, client requirements, a production API, an existing codebase, and constraints on cost and models, and must deliver a complete customer‑service agent. The benchmark evaluates performance by deploying the agent against simulated users, revealing that current state‑of‑the‑art models achieve only 23.9% success while an expert‑written reference scores 82.2%.
By Quan Shi, Keshav Dhandhania, Karthik Narasimhan, Victor Barres
E-Commerce Bench is an open‑source benchmark that simulates a year‑long e‑commerce operation, requiring LLM agents to manage multiple online stores, negotiate with suppliers, optimize sales, fulfill orders, handle returns, and manage cash flow. The environment uses real product and supplier data, a calendar of promotions and shocks, and deterministic customer and negotiation models to enable reproducible evaluation. The study evaluates 18 state‑of‑the‑art models across seven metrics, finding no single model dominates, with GPT‑5.6 Sol achieving the highest year‑end assets but lagging in fraud avoidance and operational efficiency.
By Wei Fan, Xinjie Shen, Xudong Guo, Jianhong Tu, Yang Su, Yinger Zhang, Lianghao Deng, Fengyu Wang, Baohua Dong, Yangqiu Song, Dayiheng Liu
arXiv:2509. 26331v2 Announce Type: replace Abstract: The rapid advancement of LLMs sparked significant interest in their potential to augment or automate managerial functions.
By Berdymyrat Ovezmyradov
arXiv:2606. 16613v1 Announce Type: new Abstract: As LLM agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important.
By Issa Sugiura, Daichi Hattori, Kazuo Araragi, Keita Ogawa, Shota Onose, Taro Makino, Teppei Usuki, Takashi Ishida
arXiv:2609.37658v1 Announce Type: new
Abstract: LLM agents are increasingly expected to support enterprise workflows, where tasks often involve missing information, uncertainty, feedback, and long-te...
By Min Yang, Yichen Pan, Jinghua Piao, Dandan Song, Yongshun Gong, Yong Li