🤖 AI Summary
This work addresses the challenge of maintaining consistent and auditable materialized views of world models in dynamic physical environments where sensory data is costly, heterogeneous, delayed, and unreliable. The paper introduces the first auditability-aware consistency contract for world models, combining commitment-level validity abstraction, consequence-condition-driven adaptive view maintenance, scoped transactional compensation, and an append-only provenance architecture to ensure consistency, enable efficient recovery, and support precise accountability. By integrating materialized view management, stream synchronization, Saga-style transactions, and freshness control, the proposed system demonstrates significant advantages over prior approaches on the Flood-SAR benchmark, particularly in data freshness, recovery success rate, verification overhead, and replay fidelity.
📝 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.