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English(EN) Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition

Sage-A1.5模型通过自适应组合推进长时程问题解决

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了Sage-A1.5，一个拥有350亿参数的混合专家（Mixture-of-Experts）代理模型，旨在增强长时程问题的解决能力。该模型通过学习组织工作和调整执行策略，改进了其前身A1。关键的进步包括用于任务委托和并行协作的代理编排（agentic orchestration），用于通过进度检查和策略修订来保证可靠性的代理验证（agentic verification），以及能够通过交互式分析进行探索性研究的开放式科学发现能力。 AI

影响 该模型在代理编排和验证方面的进步可以加速复杂任务的执行，并为AI驱动的科学发现开辟新途径。

排序理由 详细介绍新AI模型发布的 ist 研究论文。

在 Hugging Face Daily Papers 阅读 →

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Sage-A1.5模型通过自适应组合推进长时程问题解决

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Wenjie Lou, Tianshuo Peng, Shiyang Feng, Shengji Tang, Runmin Ma, Ruizhe Chen, Jinxin Shi, Haoyang Peng, Fangchen Yu, Zhijie Zhong, Yuhao Zhou, Yue Fan, Yixin Chen, Yiming Wu, Yiheng Wang, Yigeng Jiang, Yichen Jiang, Yi Xie, Xu Guo, Xiaohan He, Tao Hu, S… ·

    Sage-A1.5：从长时域执行到能力组合的科学探索

    arXiv:2610.28578v1 Announce Type: new Abstract: In this report, we introduce Sage-A1.5, a 35B Mixture-of-Experts agentic model that builds on A1 to advance long-horizon problem solving through adaptive capability composition. Compared with A1, Sage-A1.5 learns not only to solve i…

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

    Sage-A1.5：从长时域执行到能力组合的科学探索

    Sage-A1.5 is a 35B mixture-of-experts agentic model that improves long-horizon problem solving via adaptive task delegation, verification, and open scientific exploration.

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