AgentSkiller: 通过语义整合的跨领域数据合成提升通用智能代理能力
AgentSkiller: Scaling Generalist Agent Intelligence through Semantically Integrated Cross-Domain Data Synthesis
AI总结：
AgentSkiller通过语义整合的跨领域数据合成提升通用智能代理能力，生成高质量交互数据以增强模型功能调用性能。
摘要：
大型语言模型代理在通过工具解决现实问题方面展现出潜力，但通用智能受到高质量、长周期数据稀缺的瓶颈限制。现有方法收集隐私受限的API日志或生成缺乏多样性的脚本交互，难以产生扩展能力所需的数据。我们提出AgentSkiller，一个完全自动化的框架，用于在现实且语义关联的领域间合成多轮交互数据。它采用基于有向无环图（DAG）的架构，通过显式的状态转换确保确定性和可恢复性。该流程构建领域本体和以人物为中心的实体图，通过服务蓝图定义模型上下文协议服务器的工具接口，并用一致的数据库和严格的领域政策填充环境。跨领域融合机制将服务连接起来以模拟复杂任务。最后，该流程通过验证解决方案路径、通过执行验证过滤，并使用基于人物的模拟器生成查询，从而自动化运行。这产生了具有清晰状态变化的可靠环境。为了证明有效性，我们合成了约11K个交互样本；实验结果表明，使用此数据集训练的模型在函数调用方面显著优于基线，尤其是在较大参数规模时。
英文摘要：
Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data. Existing methods collect privacy-constrained API logs or generate scripted interactions lacking diversity, which struggle to produce data requisite for scaling capabilities. We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains. It employs a DAG-based architecture with explicit state transitions to ensure determinism and recoverability. The pipeline builds a domain ontology and Person-Centric Entity Graph, defines tool interfaces via Service Blueprints for Model Context Protocol servers, and populates environments with consistent databases and strict Domain Policies. A cross-domain fusion mechanism links services to simulate complex tasks. Finally, the pipeline creates user tasks by verifying solution paths, filtering via execution-based validation, and generating queries using a Persona-based Simulator for automated rollout. This produces reliable environments with clear state changes. To demonstrate effectiveness, we synthesized $\approx$ 11K interaction samples; experimental results indicate that models trained on this dataset achieve significant improvements on function calling over baselines, particularly in larger parameter regimes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
