arXiv AI By Yi Bin, Xiaoyang Yuan, Haoxi Zeng, Wencheng Ye, Wenqi Shao, Chen Qian, Wei Ye, Yujuan Ding, Zheng Wang, Pengpeng Zeng, Jingkuan Song, Heng Tao Shen

Terminal Agents: A Survey of AI Agents in Command-Line Environments

Read the original on arXiv AI →

The paper surveys AI agents that operate primarily through command-line terminals, defining them as systems whose main action loop involves executing terminal commands, receiving textual feedback, and interacting with a stateful environment. It introduces a seven‑dimensional terminal competence profile to link system architecture, learning, and evaluation, and highlights how behavior is jointly shaped by the model, interface, harness, runtime, and environment. The authors argue for explicit reporting of system and runtime conditions, supported by replayable traces and process‑level evidence, to better understand and benchmark terminal‑mediated agency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Oct 21

Harness Self-Evolution for Long-Horizon Terminal Agents

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By Ruhan Wang, Yucheng Shi, Zongxia Li, Junyao Yang, Zhongzhi Li, Yue Yu, Haitao Mi, Dongruo Zhou, Leoweiliang
arXiv AI
Jun 30

TUA-Bench: A Benchmark for General-Purpose Terminal-Use Agents

arXiv:2606. 28480v1 Announce Type: cross Abstract: As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding.

By Shoufa Chen, Luyuan Wang, Xuan Yang, Zhiheng Liu, Yuren Cong, Yuanfeng Ji, Feiyan Zhou, Xiaohui Zhang, Fanny Yang, Belinda Zeng
arXiv Computation and Language
Oct 12

Scaling Agentic Data for Long-Horizon Terminal Intelligence

The paper introduces TerminalHorizon, an automated engine that synthesizes long‑horizon trajectories for terminal agents by reconstructing environments from real‑world professional work and progressively expanding tasks to require further exploration and improvement. Using this engine, the authors create 1.5K task environments and fine‑tune Qwen3.5‑27B, producing TerminalHorizon‑27B, which shows significant performance gains across 12 benchmarks in coding, agentic production tasks, and reasoning. Case studies demonstrate the model’s ability to experiment beyond initial solutions, refine methods based on feedback, and preserve effective improvements in final deliverables.

By Shun Zou, Ziyu Ma, Yi Zou, Yong Wang, Lin Chen, Zehui Chen, Guanhua Chen, Xiangxiang Chu, Feng Zhao