为个人设备上的AI代理打造可验证的历史记录系统
Right to History: A Sovereignty Kernel for Verifiable AI Agent Execution
- 提出'历史权'原则，确保用户拥有可验证的代理操作记录
- 实现亚毫秒级延迟，每秒处理约400次操作，证明文件仅448字节
- 适合关注隐私与监管合规的AI系统开发者
AI代理越来越多地代表人类执行任务，但现有系统无法提供防篡改、可独立验证的操作记录。随着欧盟人工智能法案等法规要求高风险AI系统自动记录日志，这一缺失带来实际风险，尤其在个人硬件上运行的代理，其日志不受中心化平台控制。本文基于弗洛里迪的信息权利框架，将个体对自身数据的权利扩展至其委托执行的动作，提出‘历史权’：个体有权获得在其本地硬件上所有AI代理行为的完整、可验证记录。论文通过五个系统不变量形式化该原则，并在PunkGo中实现，该系统使用Rust编写，集成RFC 6962 Merkle树审计日志、基于能力的隔离、能耗预算治理及人工审批机制。对抗测试验证了所有五项不变量成立。性能评估显示，平均操作延迟低于1.3毫秒，吞吐量约400次/秒，在10,000条日志条目下，包含证明大小为448字节。
原文摘要 · Abstract (English)
AI agents increasingly act on behalf of humans, yet no existing system provides a tamper-evident, independently verifiable record of what they did. As regulations such as the EU AI Act begin mandating automatic logging for high-risk AI systems, this gap carries concrete consequences -- especially for agents running on personal hardware, where no centralized provider controls the log. Extending Floridi's informational rights framework from data about individuals to actions performed on their behalf, this paper proposes the Right to History: the principle that individuals are entitled to a complete, verifiable record of every AI agent action on their own hardware. The paper formalizes this principle through five system invariants with structured proof sketches, and implements it in PunkGo, a Rust sovereignty kernel that unifies RFC 6962 Merkle tree audit logs, capability-based isolation, energy-budget governance, and a human-approval mechanism. Adversarial testing confirms all five invariants hold. Performance evaluation shows sub-1.3 ms median action latency, ~400 actions/sec throughput, and 448-byte Merkle inclusion proofs at 10,000 log entries.
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