ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation
Zhiqin Yang, Zhenyuan Zhang, Xianzhang Jia, Jun Song, Wei Xue, Yonggang Zhang, Yike Guo

TL;DR
ClawNet introduces a human-symbiotic agent network enabling secure, cross-user collaboration by leveraging layered identity architecture, scoped authorization, and accountability logs, addressing the lack of infrastructure for multi-user agent cooperation.
Contribution
The paper proposes a novel human-symbiotic agent paradigm with a layered identity system and governance primitives, and implements it in ClawNet for secure multi-user agent collaboration.
Findings
ClawNet enforces identity binding and authorization verification.
The framework enables secure collaboration among multiple users.
Accountability logs ensure full auditability of agent actions.
Abstract
Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user. Human productivity rests on the social and organizational relationships through which people coordinate, negotiate, and delegate. When agents move beyond performing tasks for one person to representing that person in collaboration with others, the infrastructure for cross-user agent collaboration is entirely absent, let alone the governance mechanisms needed to secure it. We argue that the next frontier for AI agents lies not in stronger individual capability, but in the digitization of human collaborative relationships. To this end, we propose a human-symbiotic agent paradigm. Each user owns a permanently bound agent system that collaborates on the owner's behalf, forming a network whose nodes are humans rather than agents. This paradigm rests on three…
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