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New framework uses scientific software to train AI terminal agents

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a self-supervised framework called software-in-the-loop reconstruction (SWR) to create training environments for terminal agents in scientific domains. This method leverages existing scientific software workflows to generate reference behaviors and verification targets, reducing the need for manual engineering. When applied to Qwen3.8-Max, the framework facilitated the solution of 838 tasks and produced over 1,400 verified trajectories. Further fine-tuning of Qwen3.8-27B using this data significantly improved performance on the Terminal-Bench 2 benchmark, indicating the potential for scalable supervision from scientific software. AI

IMPACT Enables scalable training of AI agents for scientific tasks by leveraging existing software, potentially accelerating AI adoption in research.

RANK_REASON The cluster describes a research paper detailing a new framework and benchmark for training AI agents, including performance results for specific models.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework uses scientific software to train AI terminal agents

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhongzhi Li, Yucheng Shi, Zongxia Li, Junyao Yang, Ruhan Wang, Yu Wang, Jingyuan Huang, Jichao Yu, Ninghao Liu, Haitao Mi, Leowei Liang ·

    Self-Supervised Scaling of Terminal Environments for Scientific Domains

    arXiv:2610.02710v1 Announce Type: cross Abstract: Terminal agents are increasingly deployed beyond software engineering in science and other specialized domains. Constructing training environments requires executable reference behavior and a domain-specific verifier that distingu…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Self-Supervised Scaling of Terminal Environments for Scientific Domains

    Terminal agents are increasingly deployed beyond software engineering in science and other specialized domains. Constructing training environments requires executable reference behavior and a domain-specific verifier that distinguishes semantic correctness from superficially plau…

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