Llm Extraction Defense And Physics-Editable World Models beyond Nominal Performance: Mechanisms, Uncertainty, and Deployment

Abstract

Research spanning LLM extraction defense and physics-editable world models increasingly joins methods that were developed for different objects and decisions. Here, a honeypot knowledge graph that redirects model-extraction queries toward low-transferability knowledge is compared with a large-scale dataset organized around physically editable world-model factors to determine which claims can travel across those boundaries and which remain context dependent. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: attack modeling, honeypot knowledge, query economics, utility preservation, adaptive attackers. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. Comparison reveals recurring trade-offs among attack modeling, honeypot knowledge, and query economics. These trade-offs do not support a universal ranking; instead, they identify the operating envelope within which each method remains credible and the perturbations most likely to expose fragile conclusions. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.