让谈判智能体在本地运行，既快又保护隐私。
Device-Native Autonomous Agents for Privacy-Preserving Negotiations
- 智能体在用户设备上自主决策，不上传敏感数据
- 成功率87%，延迟比云端低2.4倍，零知识证明保隐私
- 适合保险、企业采购等重视隐私的场景
保险和企业间（B2B）自动化谈判面临重大挑战。现有系统为追求便利性，将敏感财务数据经由中心化服务器传输，增加安全风险并降低用户信任。本文提出一种设备原生的自主代理式人工智能系统，实现隐私保护下的谈判。该系统仅在用户硬件上运行，支持实时议价并本地维护敏感约束。通过集成零知识证明保障隐私，并采用蒸馏世界模型支持设备端高级推理。架构包含六个技术组件，嵌入于代理式AI工作流中。代理可自主规划谈判策略，开展安全多方谈判，并生成加密审计日志，全程不向外部服务器暴露用户数据。系统在保险与B2B采购场景下，于多种设备配置上评估。结果显示平均成功率达87%，相较于云基线延迟降低2.4倍，且通过零知识证明实现强隐私保护。用户研究显示，当可查看决策轨迹时，信任度提升27%。这些成果为隐私敏感金融领域可信自主代理奠定了基础。
原文摘要 · Abstract (English)
Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Agentic AI system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an Agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87 %, a 2.4x reduction in latency relative to cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27 % higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.
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