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EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies

NeurIPS 2026 · Multi-agent systems and games › LLM agents · approach: Robust and efficient agent execution · 0 citations (OpenAlex)

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Introduces EcoGym, an open benchmark of three interactive economic environments (Vending, Freelance, Operation) for evaluating LLM agents on long-horizon plan-and-execute decision making over up to 365 simulated days.

Problem

Existing long-horizon agent benchmarks are episodic, domain-specific, or closed, and do not capture persistent economic dynamics with hidden mechanics and partial observability.

Approach

A unified, budgeted-action decision framework with three economic environments and hidden latent dynamics (demand elasticity/seasonality, burnout thresholds, DAU decay), scored by business outcomes (net worth, income, DAU) over an effectively unbounded horizon; open-sources an adapted Vending-Bench and adds Freelance and Operation environments.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: LiveCodeBench SWE-bench FinQA BizFinBench GSM8K SciQ LEXam Pile-of-law

Benchmarks: EcoGym Vending EcoGym Freelance EcoGym Operation

Metrics: net worth income DAU

Baselines: Claude-Sonnet-4.5 Gemini-3-Pro Gemini-3-Flash GPT-5.2 Kimi-K2.6 Qwen3.5-397b-A17b DeepSeek-v3.2 GLM-5 MiniMax-M2.7 Human experts

Builds on: Vending-Bench

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