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Structured State Reconciliation for Human-AI Task Handover — arXiv:2608.28907

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28907
License (as archived): CC0-1.0
Archived: 2026-10-17T07:47:22.481Z
SHA-256 of the archived text: 6863f861fb4d2faabd320657a4967b0e6a09014cf8134acfd67ed2e10d67eb30
Stable URL of this page: https://metraipolis.com/documento/dc_7d231d5b60cf

How to cite

Structured State Reconciliation for Human-AI Task Handover — arXiv:2608.28907 — arXiv (en), https://arxiv.org/abs/2608.28907 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7d231d5b60cf (Metraipolis archive dc_7d231d5b60cf, sha256 6863f861fb4d2faabd320657a4967b0e6a09014cf8134acfd67ed2e10d67eb30)

BibTeX

@misc{dc7d231d5b60cf,
  title = {Structured State Reconciliation for Human-AI Task Handover — arXiv:2608.28907},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28907},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7d231d5b60cf; sha256 6863f861fb4d2faabd320657a4967b0e6a09014cf8134acfd67ed2e10d67eb30},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28907. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.AI).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.28907 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:28:26.700Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).