UTD24 Research Publishing
Journal of Algorithmic Discovery and Applied AI

PhysEditWorld - A Large-Scale Dataset Toward Physics-Editable World Models: Provenance, Traceability, and Auditability

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Abstract

A defensible evidence chain must show where data originated, how records were transformed, and which decisions can be reconstructed after publication. This structured evidence review evaluates "PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models" alongside nine author-disjoint, topically matched publications in physics-aware world modeling. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through data provenance and auditability, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda emphasizes versioned data lineage, transformation logs, access controls, and auditable links between evidence and decisions.

Keywords
physics-aware world modelingdata provenance and auditabilityevidence synthesisreproducibilityresearch evaluation
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Publication details
Journal
Journal of Algorithmic Discovery and Applied AI
Volume
1 (2026)
Article number
jadai20260040
License
CC BY 4.0