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This paper proposes TRACE-RealWorld, a system for maintaining auditable consistency contracts over world models treated as materialized views in dynamic physical environments. Rather than introducing novel predictive architectures, it focuses on the data management infrastructure required to handle fallible sensing, delayed reads, and transaction-style recovery for learned representations. The work establishes critical protocols for accountability and replayability when deploying AI-driven operational systems that interact with real-world states.
TRW: TRACE-RealWorld---An Auditable Consistency Contract for World Models as Materialized Views
arXiv:2607.21910v1 Announce Type: new Abstract: TRACE-RealWorld addresses a core data-management problem: maintaining an actionable materialized view over a continuously changing physical world when reads of the base state are priced, delayed, heterogeneous, and fallible. Its data-management contributions are a commitment-level validity abstraction for materialized predictions; consequence-conditioned adaptive view maintenance; transaction-style, dependency-scoped compensation for commitments invalidated after authorization; and append-only provenance supporting exact replay. The work builds directly on materialized-view maintenance, adaptive stream synchronization, transaction recovery, sagas, data freshness, and provenance. The end-to-end Flood-SAR evaluation treats sensing as physical data acquisition and measures freshness, verification cost, stale reads, recovery scope, restoration failure, and replayability through six pre-registered questions with held-out seeds. The contribution is therefore not a new predictive model, but a consistency, recovery, and accountability contract for deploying learned world representations as operational data systems.
Abstract:TRACE-RealWorld addresses a core data-management problem: maintaining an actionable materialized view over a continuously changing physical world when reads of the base state are priced, delayed, heterogeneous, and fallible. Its data-management contributions are a commitment-level validity abstraction for materialized predictions; consequence-conditioned adaptive view maintenance; transaction-style, dependency-scoped compensation for commitments invalidated after authorization; and append-only provenance supporting exact replay. The work builds directly on materialized-view maintenance, adaptive stream synchronization, transaction recovery, sagas, data freshness, and provenance. The end-to-end Flood-SAR evaluation treats sensing as physical data acquisition and measures freshness, verification cost, stale reads, recovery scope, restoration failure, and replayability through six pre-registered questions with held-out seeds. The contribution is therefore not a new predictive model, but a consistency, recovery, and accountability contract for deploying learned world representations as operational data systems.
Artificial Intelligence (cs.AI); Databases (cs.DB)
H.2.4; I.2.7; C.2.4
arXiv:2607.21910 [cs.AI]
(or arXiv:2607.21910v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.21910 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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