发表机构
University of Colorado Boulder（科罗拉多大学博尔德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对人机任务交接中系统记录与人类报告的信息分散问题，提出溯源感知的结构化状态协调流程，实验表明其在保留任务状态效用的同时错误信息更少，是安全AI辅助交接的有效设计模式。
AI 中文摘要
任务交接需要传递足够的当前状态信息以便继任者恢复工作，但相关信息往往分散在系统记录和人类观察中。系统记录精确且带有时间戳，但仅能部分观测任务；人类报告包含日志中没有的意图和任务知识，却易出现遗漏和记忆错误。我们提出一种溯源感知的流程，将任务遥测数据和人类撰写的报告转换为共享的类型化任务状态表示，对齐并协调二者的事实，检测冲突并生成结构化交接报告。我们在受控空间多任务环境中收集的13组配对任务状态上评估该方法，使用基于任务的指标估计报告能为假设接收者节省的状态重建成本，以及其带来的错误信息负担。协调两种来源相比单独使用用户报告或遥测数据，保留了更高的估计任务状态效用。与给定相同输入的直接端到端LLM相比，结构化协调保持了相当的估计效用，同时错误信息显著更少；任务感知渲染相比详尽渲染，每token更高效地保留效用。探索性内容分析进一步显示，人类报告包含大量状态聚焦指标之外的战略知识。这些结果支持溯源感知状态协调作为更安全的AI辅助交接的设计模式。
英文摘要
Task handover requires communicating enough current state for a successor to resume work, yet the relevant information is often divided between system records and human observations. System records can be precise and timestamped but only partially observe the task, while human reports capture intent and task knowledge that no log contains but are vulnerable to omission and memory error. We present a provenance-aware pipeline that converts task telemetry and human-authored reports into a shared typed task-state representation, aligns and reconciles their facts, detects conflicts, and generates structured handover reports. We evaluate the approach on 13 paired task states collected in a controlled spatial multitask environment, using task-grounded metrics that estimate the state-reconstruction cost a report would spare a hypothetical recipient and the misinformation burden it would impose. Reconciling both sources preserved greater estimated task-state utility than either the user report or telemetry alone. Relative to a direct end-to-end LLM given the same inputs, structured reconciliation maintained comparable estimated utility while incurring substantially less misinformation, and task-aware rendering retained utility more efficiently (per token) than exhaustive rendering. An exploratory content analysis further shows that human reports contain substantial strategic knowledge that lies outside state-focused metrics. These results support provenance-aware state reconciliation as a design pattern for safer AI-assisted handover.
CommentsIn preparation for conference submission
