arXiv AI

Business Arena: Benchmarking LLM Agents in a Realistic Marketplace

arXiv:2608. 08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work.

arXiv AI

Jul 14

Can Agentic Trading Systems Pay for Their Own Intelligence?

arXiv:2607. 10286v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce trading value.

By Qiqi Duan, Changlun Li, Chen Wang, Fan Zhang, Mengxiang Wang, Dayi Miao, Peixian Ma, Jiangpeng Yan, Liyuan Chen, Shuoling Liu, Preslav Nakov, Yuyu Luo, Nan Tang

llms agents

arXiv AI

Jun 18

CEO-Bench: Can Agents Play the Long Game?

arXiv:2606. 18543v1 Announce Type: new Abstract: Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service.

By Haozhe Chen, Karthik Narasimhan, Zhuang Liu

llms agents benchmarks

arXiv AI

Aug 7

AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations

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

llms benchmarks

arXiv AI

Jun 16

RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments

arXiv:2606. 15862v1 Announce Type: new Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain.

By Linghua Zhang, Jun Wang, Jingtong Wu, Zhisong Zhang

llms agents benchmarks

arXiv AI

Aug 3

MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations

arXiv:2607. 28956v1 Announce Type: new Abstract: Large language model agents are increasingly evaluated as autonomous tool users, yet most benchmarks focus on bounded tasks with immediate success criteria.

By Qiming Shi, Yulong Tao, Linbo Jin, Zhaolu Kang, Yibo Dou, Jiawen Zhu, Tianjun Pan, Shaokang Fu, Chengyu Wang, Siyue Li, Yaping Cheng, Di Weng, Chengfu Huo

llms agents benchmarks

arXiv AI

Jul 10

RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments

arXiv:2603. 16453v3 Announce Type: replace Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain.

By Linghua Zhang, Jun Wang, Jingtong Wu, Zhisong Zhang

llms agents benchmarks

arXiv AI

Jun 2

Business Utility of Large Language Models as Exploratory Data Analysis Agents

arXiv:2606. 00051v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in analytical workflows, but their suitability as exploratory data analysis (EDA) agents in business settings remains uncertain.

By Rafa{\l} {\L}ab\k{e}dzki, Patryk Miziu{\l}a, Hubert Rutkowski, Szymon Betlewski, Cezary Depta, Szymon Janowski, Jaros{\l}aw Kochanowicz, Jan Kanty Milczek

llms agents benchmarks

arXiv AI

Jun 30

CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents

arXiv:2606. 29771v1 Announce Type: new Abstract: LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading.

By Bo Qu, Mingguang Chen

llms agents benchmarks safety

arXiv AI

Jun 9

Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems

arXiv:2606. 08285v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling.

By Junyi Yao, Zihao Zheng

llms agents

arXiv AI

Aug 11

When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains

arXiv:2608. 07538v1 Announce Type: new Abstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably, and avoid money-losing contracts.

By Chen Liang, Fasheng Xu

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jun 16

CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies

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

llms agents benchmarks

arXiv AI

Jun 17

Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation

arXiv:2606. 17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings.

By Yuyang Dai, Xueqing Peng, Lingfei Qian, Zhuohan Xie

llms agents benchmarks

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