R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models
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
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- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
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- LinkedLinked via arxiv author · 85%Qiwen Gu →
“R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models”
- LinkedLinked via arxiv author · 85%Bingjie Gao →
“R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models”
- LinkedLinked via arxiv author · 85%Kerui Chen →
“R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models”
- LinkedLinked via arxiv author · 85%Yungeng Liu →
“R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models”
- LinkedLinked via arxiv author · 85%Jifan Li →
“R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models”
- LinkedLinked via arxiv author · 85%Qishuai Wen →
“R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models”
- LinkedLinked via arxiv author · 85%Li Niu →
“R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models”