Structured State Reconciliation for Human-AI Task Handover
Human-Computer InteractionArtificial Intelligence
Summary
The authors studied how to help pass on work between people by combining computer data and human notes about a task. They created a system that merges these two sources, checks for conflicts, and makes clear reports for the next person taking over. Their tests showed that using both data and human input together worked better than using either alone, and was safer than relying only on large language models. They also found that human notes include useful strategic details not captured by standard task records. Overall, their method helps create more reliable and useful handover information.
task handoverprovenance-aware pipelinetask telemetryhuman-authored reportsstate reconciliationtask-state representationmisinformation burdenLLM (Large Language Model)state reconstructioncontent analysis
Authors
Kayleigh Bishop, Maria P. Stull, Breanne Crockett, Bradley Hayes
Abstract
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.