Nex-N1：通过统一生态系统进行大规模环境构建训练的智能体模型

摘要

大型语言模型从被动响应者向自主智能体的演进，亟需学习范式的根本性转变——从静态模仿转向激励机制驱动的决策过程。然而，由于缺乏能够构建高质量交互信号以实现有效策略学习的可扩展基础设施，这一转变受到严重制约。为此，我们提出一套系统性扩展交互环境多样性与复杂度的综合方案。该方案通过三个正交维度实现规模化构建：（1）复杂度：NexAU灵活智能体框架支持通过简易配置构建复杂智能体层级；（2）多样性：NexA4A从自然语言自动生成多样化智能体层级以覆盖无限领域；（3）保真度：NexGAP通过集成动态现实环境实现具身轨迹合成，弥合模拟与现实的鸿沟。基于我们基础设施构建的多样化复杂交互环境，我们训练出Nex-N1模型。在SWE-bench和tau2等基准测试中的实证结果表明，Nex-N1在复杂智能体任务上持续超越开源SOTA模型，并与前沿专有模型展现出竞争性表现。我们开源Nex生态系统及模型权重以促进后续研究。

English

The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incentive-driven decision making. However, this transition is significantly impeded by the lack of scalable infrastructure capable of constructing high-quality interaction signals for effective policy learning. To address this, we introduce a comprehensive method designed to systematically scale the diversity and complexity of interactive environments. Our method realizes this scaling by addressing three orthogonal dimensions: (1) Complexity: NexAU, a flexible agent framework that supports building complex agent hierarchies via simple configurations; (2) Diversity: NexA4A automatically generates diverse agent hierarchies from natural language to cover infinite domains; and (3) Fidelity: NexGAP bridges the simulation-reality gap by integrating dynamic real-world environment for grounded trajectories synthesis. We train Nex-N1 upon the diverse and complex interactive environments established by our infrastructure. Empirical results on benchmarks such as SWE-bench and tau2 demonstrate that Nex-N1 consistently outperforms SOTA open-source models and achieves competitive performance against frontier proprietary models on complex agentic tasks. We open-source the Nex ecosystem and model weights to facilitate further research.

Nex-N1：通过统一生态系统进行大规模环境构建训练的智能体模型

Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction

摘要

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