NexForge proposes a demand-driven framework that can automatically synthesize diverse executable agent tasks and expert trajectories without domain-specifi...
NexForge proposes a demand-driven framework that can automatically synthesize diverse executable
agent tasks and expert trajectories without domain-specific infrastructure. The framework generates 3.6K terminal and 2K office tasks, improving Qwen3.5-35B-A3B Base from 22.5% to 52.0% on Terminal-Bench 2.0, and raising GDPval Elo from 813 to 1338.
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NexForge提出需求驱动的任务合成框架，可在无需领域特定基础设施的条件下，自动生成多样化、可执行的智能体任务及专家轨迹。
该研究属于论文研究，来源为HuggingFace Daily Papers社区热门论文。现有材料仅提供摘要数据，正文摘录为arXivLabs通用介绍。
Aioga判断，NexForge的核心关注点是以需求驱动方式扩展训练任务供给；其价值仍需结合完整论文的方法、实验设置与复现结果评估。
摘要称，框架生成3.6K终端任务和2K办公任务，并使Qwen3.5-35B-A3B Base在两项所列评测中的成绩提高，值得关注。 建议后续核查完整论文，重点确认任务合成流程、专家轨迹来源、基准评测条件、对照实验及结果可复现性，避免仅凭摘要作出推广判断。
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Ingestion channel: Summary aggregation · Source domain: arxiv.org
Source: HuggingFace Daily Papers（社区热门论文）
Original link: Open original source
Aioga archive: Open intelligence page
Content record: source-page · Updated: 2026-07-17T00:00:00.000Z
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