Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception

arXiv 2026 28.6 method

TLDR

Certified world models provide drift-aware sensing deadlines for active perception, validated on frozen VN-JEPA and synthetic benchmarks.

Reasoning

The paper introduces a novel method to derive operational sensing clocks from certified world models, with a focus on drift-awareness. Strengths include clear theoretical derivation and empirical validation on both a frozen video model and a synthetic bench. Weaknesses are the lack of real-world experiments and limited advantage over non-spectral schedulers in certain regimes.

Read-first score

Read-first score 28.6, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 29.

Recency 6%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Topical relevance 29%

41.4

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Methodology quality 18%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=result

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Citation impact 18%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 12%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 120.

Keyword Scores

world model

world dynamics prediction

video world model

world simulator

generative world model

interactive world model

model-based reinforcement learning world model

Resources

Paper