Device-Native Autonomous Agents for Privacy-Preserving Negotiations
Joyjit Roy, Samaresh Kumar Singh

TL;DR
This paper presents a device-native autonomous AI system for privacy-preserving negotiations that operates on user hardware, ensuring secure, real-time bargaining with high success rates and increased user trust.
Contribution
It introduces a novel on-device negotiation architecture using zero-knowledge proofs and world models, enhancing privacy and reducing latency compared to cloud-based systems.
Findings
87% success rate in negotiations
2.4x reduction in latency
27% higher user trust scores
Abstract
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…
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