EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies
Xavier Hu, Jinxiang Xia, Shengze Xu, Kangqi Song, Yishuo Yuan, Guibin Zhang, JinCheng Ren, Boyu Feng, Li Lu, Tieyong Zeng, Jiaheng Liu, Minghao Liu, He Zhu, Yuchen Eleanor Jiang, Wei Wang, Wangchunshu Zhou

TL;DR
EcoGym is a new benchmark for evaluating long-horizon decision-making in interactive economies, assessing LLMs' strategic coherence and robustness over extended, stochastic scenarios.
Contribution
We introduce EcoGym, a versatile, open-source benchmark with three environments for continuous plan-and-execute tasks in economic settings, enabling comprehensive LLM evaluation.
Findings
No single LLM dominates across all scenarios.
Models show significant suboptimality in strategy and execution.
EcoGym reveals systematic challenges in long-term economic decision-making.
Abstract
Long-horizon planning is widely recognized as a core capability of autonomous LLM-based agents; however, current evaluation frameworks suffer from being largely episodic, domain-specific, or insufficiently grounded in persistent economic dynamics. We introduce EcoGym, a generalizable benchmark for continuous plan-and-execute decision making in interactive economies. EcoGym comprises three diverse environments: Vending (adapted from the closed-source Vending-Bench, with full open-source release), Freelance (new), and Operation (new), implemented in a unified decision-making process with standardized interfaces, and budgeted actions over an effectively unbounded horizon (1000+ steps if 365 day-loops for evaluation). The evaluation of EcoGym is based on business-relevant outcomes (e.g., net worth, income, and DAU), targeting long-term strategic coherence and robustness under partial…
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