Terminal-World: Large-Scale Environment Synthesis for Terminal Agents

The paper presents Terminal-World, an automated pipeline that uses Agent Skills to generate diverse terminal‑agent training data, builds over 5,700 environments, and trains models that outperform existing baselines on multiple benchmarks despite using far less data.

Agent SkillsTerminal-Worldbenchmark

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Terminal-World: Large-Scale Environment Synthesis for Terminal Agents

Machine Heart

May 5, 2026 · Artificial Intelligence

Agent-World: Scaling Real-World Environments for Co‑Evolving Agents and Their Worlds

Agent-World introduces a universal training arena that automatically mines real‑world data from the internet to build over 1,900 diverse environments and 19,800 tools, then generates long‑horizon tasks through graph‑based and programmatic synthesis, creating a self‑evolving loop where agents are evaluated, diagnosed, and the environment is refined, achieving state‑of‑the‑art results on 23 benchmarks.

AI agentsAgent-Worldbenchmark evaluation

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Agent-World: Scaling Real-World Environments for Co‑Evolving Agents and Their Worlds