SOURCE-LINKED INTELLIGENCE

R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

High similarity between first-visit and return frames does not necessarily show that a video world model remembered the scene; the intervening rollout may simply have changed very little. This ambiguity makes absolute revisit scores sensitive to rendering stability, repetitive content, and failed motion. We introduce \emph{R2M-Bench} (\textbf{R}elative \textbf{R}evisit \textbf{M}emory Benchmark), a benchmark of observable revisit-selective consistency. For every detected return, R2M-Bench compares the revisit pair with two controls from the same rollout: a gap-matched non-revisit pair that mea

Read original source ↗ Open in workspace

recordType

paper

region

Global