Unified Proof-Action Trace Pack for High-Risk AI Action Auditing

This dataset contains a strict public minimal proof-action trace pack for auditing high-risk AI action systems. It maps candidate wait, repair, and denial events into a unified schema covering authority state, contract closure, negative-space memory, regret computability, denominator credit, reward credit, clean-learning credit, leakage checks, and file-level provenance. The pack supports auditability, restraint, closure-gap analysis, no-credit repair discipline, and public evidence timestamping. It intentionally excludes private event-level trace logs. It does not claim trading profit, autonomous execution, real-robot deployment, or third-party validation.本数据集面向高风险AI行动系统的审计需求，提供一套严格公开的最小化证明-行动轨迹包（proof-action trace pack）。该轨迹包将候选等待、修复与拒绝事件映射至涵盖以下维度的统一模式：权限状态、合约闭合、负空间记忆、可悔性计算（regret computability）、分母信用、奖励信用、清洁学习信用、泄漏检测及文件级溯源。该轨迹包支持可审计性、约束管控、闭合间隙分析、无信用修复准则以及公开证据时间戳服务。其有意排除私有事件级轨迹日志。本数据集未声明可实现交易盈利、自主执行、实体机器人部署或第三方验证。